Range Weighted Branch Length Difference (RWiBaLD) Calculations & Figure Generation

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Introduction

This document contains code for calculating and plotting Range Weighted Branch Length Difference (RWiBaLD). It uses input from Biodiverse analyses of phylogenetic data. The method is related to the Categorical Analysis of Neo And Paleo-Endemisim (CANAPE) published in 2014 and follows up on that paper. Consequently, the acacia dataset from the CANAPE paper is used to illustrate the method.

Load packages

Here we load the packages for use in subsequent code.

library(sf)
library(ggplot2)
library(Cairo)
library(extrafont)
library(dplyr)
library(ggrepel)
library(stringr)
library(patchwork)
library(forcats)
library(ggtree)
library(ape)
library(knitr)
library(gt)

The RWiBaLD metric

The steps for calculating RWiBaLD are as follows:

1. For each branch on the phylogeny (terminal or internal), calculate the difference between its branch length on the Range Weighted Observed Tree (RWoT), and its branch length on the Range Weighted Comparison Tree (RWcT) to generate its Range Weighted Branch Length Difference (RWiBaLD) score. 

2. Identify the branches that contribute the most to PE; we call these the “highly endemic branches” or “branches of key interest”. These are defined as those with the smallest observed range sizes, i.e. the longest branches on the RWcT. We calculate this by ranking all branches according to the inverse of their range size (functionally the same as their lengths on the RWcT), plotting a straight line from the first to last points on that curve, then identifying the point on the distribution that is furthest from that line (figure 2A). Weighted ranges greater than or equal to the threshold are considered highly endemic.

3. The same elbow statistic is then applied separately to each half of the distribution of ranked RWiBaLD score values (divided at the RWiBaLD = 0 value) to identify branch length difference thresholds.

4. The classification is then applied to the highly endemic branches of step 2 using the thresholds from step 3.  Highly endemic branches with negative differences less than or equal to the threshold are classified as neo-endemic, positive differences greater than or equal to the threshold are classified as paleo-endemic, and those between these two categories are classified as meso-endemic (figure 2B).

Specify biodiverse results data

Specify the tabular data files exported from a biodiverse analysis. Both the observed data & the equal branch length data from a range weighted tree.

# Set the data locations etc.
data_dir <- "Acacia_biodiverse_exports/"
observed_data_csv <- paste0(data_dir, "Acacia_RWT_tabular_export.csv")
equal_branch_length_data_csv  <- paste0(data_dir, "Acacia_RWT_EQBL_tabular_export.csv")

Load data & calculate RWiBaLD

Load the data in, merge the tables and calculate the RWiBaLD score for each branch, then write out the results to a file.

# Load data
observed_data <- read.table(observed_data_csv, header=T,sep=",")
equal_branch_length_data <- read.table(equal_branch_length_data_csv, header=T,sep=",")

# Calculate RWiBaLD Statistic etc.
rwibald_results <- observed_data |>
  left_join(equal_branch_length_data, by = 'NAME') |>
  rename(branch_length_observed_tree = LENGTH.x, branch_length_comparison_tree = LENGTH.y) |> 
  mutate(rank_comparison_tree = rank(desc(branch_length_comparison_tree), ties.method = "min")) |>
  mutate(sorted_asc_bl_comparison_tree = rank(branch_length_comparison_tree, ties.method = "first")) |>
  mutate(rwibald_score = (branch_length_observed_tree - branch_length_comparison_tree)) |>
  mutate(rwibald_score_rank = rank(rwibald_score, ties.method = "first"))

# View(rwibald_results)
output_dir <- "quarto_outputs/"

# Write data to file
write.csv(rwibald_results, paste0(output_dir, "Acacia_RWiBaLD_results.csv"), row.names=FALSE)

Elbow point calculation function

We propose a function to calculate the elbow point of a curve in order to determine branches of interest and identify the different categories of RWiBaLD (e.g., neo, meso, paleo).

It takes a numerical vector data as input and calculates the point where the rate of decrease in a measure of fit (e.g., sum of squared distances) starts to slow down. The function first orders the values of the data and calculates vectors from the first point to all other points, normalizing the vector from the first to the last point. It then computes the Euclidean distance of each point to this line and returns the y-coordinate (value) of the point with the maximum distance from the line, considered the “elbow” point threshold.

The procedure is described step by step below:

1. Function Definition and Input:

   • The function get_elbow takes one argument, data, which is a numerical vector.

2. Initial Setup:

   • n_pts is assigned the length of data.

   • x_coords is a sequence from 1 to n_pts.

   • y_coords is the values of data, sorted in ascending order.

3. First Point Coordinates:

   • x1 and y1 are the coordinates of the first point in the sequence.

4. Normalize the Line Vector:

   • x_vec and y_vec are vectors from the first point to all other points.

   • x_vec_max and y_vec_max are the vectors from the first point to the last point.

   • normaliser is the length of the line vector.

   • Normalize x_vec_max and y_vec_max by dividing by normaliser.

5. Vectors from First Point:

   • v_x and v_y are vectors from the first point to all other points.

   • scalar_prod calculates the scalar projection of v_x and v_y onto the normalized line vector.

6. Calculate Distance to Line:

   • vec_to_line_x and vec_to_line_y are the components of the vectors from each point to the line.

   • dist_to_line is the Euclidean distance of each point to the line.

7. Return the Elbow Point Threshold:

   • The function returns the y-coordinate (value) of the point with the maximum distance from the line, considered the “elbow” point threshold.

In summary, the function identifies the point in the data set that is farthest away from a line drawn between the first and last data points, which often corresponds to a significant change.

get_elbow <- function(data){
    
   n_pts = length(data)

   x_coords = 1:n_pts
   y_coords = data[order(data)]

   # First points
   x1 = x_coords[1]
   y1 = y_coords[1]

   # Normalize the line vector
   x_vec = x_coords - x1
   y_vec = y_coords - y1
   x_vec_max = x_vec[n_pts]
   y_vec_max = y_vec[n_pts]

   normaliser = sqrt(x_vec_max^2 + y_vec_max^2)

   x_vec_max = x_vec_max / normaliser
   y_vec_max = y_vec_max / normaliser

   # Vectors from first point
   v_x = x_coords - x1
   v_y = y_coords - y1

   scalar_prod = v_x * x_vec_max + v_y * y_vec_max

   vec_to_line_x = v_x - scalar_prod * x_vec_max
   vec_to_line_y = v_y - scalar_prod * y_vec_max

   # Distance to line is the norm
   dist_to_line = sqrt(vec_to_line_x^2 + vec_to_line_y^2)
   
   # y value at the point of maximum distance from the hypotenuse
   return(y_coords[which(dist_to_line==max(dist_to_line))])
}

Calculate branches of interest

Now we take the data and determine the key branches of interest using the elbow point statistic defined in the function above.

# For range weighted tree
df <- rwibald_results[,c("NAME", "rwibald_score", "rwibald_score_rank",
                         "sorted_asc_bl_comparison_tree",
                         "branch_length_comparison_tree",
                         "branch_length_observed_tree")] %>%
      arrange(., branch_length_comparison_tree)

# Call the get_elbow function to find the threshold
elbow_thresh <- get_elbow(df$branch_length_comparison_tree)

# Define key branches
key_branches <- df$branch_length_comparison_tree >= elbow_thresh

# Count them
number_of_key_branches <- sum(key_branches)

# Add a column to the results marking the branches of interest as 'sig' 
rwibald_results_key <- rwibald_results %>%
  arrange(., branch_length_comparison_tree) %>%
  mutate(bl_comparison_tree_rwibald_elbow_key = ifelse(key_branches, 'key', 'not_key'))

# Merge data
rwibald_results_all <- merge(rwibald_results, rwibald_results_key[, c("NAME","bl_comparison_tree_rwibald_elbow_key")], by=c('NAME','NAME'),all.x=T)

# View(rwibald_results_all)

# Print number of key branches
print(paste0("Number of key branches: ", number_of_key_branches))

[1] "Number of key branches: 135"

Generate figure 2A - RWiBaLD branches of interest

Now we plot the ranked branch lengths on the comparison tree showing the cut-off line for the key RWiBaLD branches of interest.

# Check the total number of branches
# length(df$branch_length_comparison_tree)

# Add x_marks every 100 
x_marks <- c(0, 100, 200, 300, 400, 500, 600, 700, 800, 900, length(df$branch_length_comparison_tree))

branch_length_by_rank <- ggplot(df, aes(x = as.numeric(rownames(df)), y = sort(branch_length_comparison_tree))) +
  geom_point() +
  geom_vline(xintercept = (length(df$branch_length_comparison_tree) - number_of_key_branches+1), color = "red", linetype = "dashed") +
  xlab("Branch Length Rank (shortest-longest)") +
  ylab("Branch Length\nComparison Tree (RWcT)") + 
  theme_classic() + 
  theme(plot.title = element_text(hjust = 0.5)) +
  scale_x_continuous(breaks = x_marks, labels = x_marks) +
  annotate("text", x = 970, y = 0.008, label = paste0(number_of_key_branches, " key\n branches\n of interest"), vjust = 1, hjust = 0.5)

print(branch_length_by_rank)

output_dir <- "quarto_outputs/"

ggsave(paste0(output_dir, "figures/Figure2_A.png"), plot = branch_length_by_rank,  scale = 1,
        width = 2048,
        height = 1024,
        units = "px",
        dpi = 300)

Calculate RWiBaLD branch categories (neo-endemic, meso-endemic, paleo-endemic)

Next we take the key branches of interest and define which of them are neo-endemic, meso-endemic or paleo-endemic. We use the same elbow statistic defined above but only consider the branches of key interest calculated above as valid. We split the data at 0 with those equal or below the elbow point threshold in the negative data being neo-endemics. Those in the positive dataset equal or above the elbow point threshold being classified as paleo-endemic, and rest are classified as meso-endemic. We have described three categories, but of course one could break the continuous distribution into further categories if required.

# Load the data
data <- rwibald_results_all[,c("NAME", "bl_comparison_tree_rwibald_elbow_key", "rwibald_score", "rwibald_score_rank")] %>%
  arrange(., rwibald_score_rank)

# Subset the left and right dataframes
subset_left_df <- data[data$rwibald_score <= 0, ]
subset_right_df <- data[data$rwibald_score >= 0, ]

# Create a list to store the results for the table
results <- list()

# Records in negative data
negative_records <- nrow(subset_left_df)
results$Negative_Records <- negative_records

# Neo cutoff
left_point_x <- get_elbow(subset_left_df$rwibald_score)
rwibald_score_rank_at_left_point <- subset_left_df[subset_left_df$rwibald_score == left_point_x, "rwibald_score_rank"]
results$Neo_Cutoff <- rwibald_score_rank_at_left_point

# Plot for left half of curve
l <- ggplot(subset_left_df, aes(x = rwibald_score_rank, y = rwibald_score)) +
  geom_point() +
  geom_vline(xintercept = rwibald_score_rank_at_left_point, color = "red", linetype = "dashed") +
  ggtitle(label= "Neo cutoff") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5))
print(l)

# Records in positive data
positive_records <- nrow(subset_right_df)
results$Positive_Records <- positive_records

# Paleo cutoff
right_point_x <- get_elbow(subset_right_df$rwibald_score)
rwibald_score_rank_at_right_point <- subset_right_df[subset_right_df$rwibald_score == right_point_x, "rwibald_score_rank"]
results$Paleo_Cutoff <- rwibald_score_rank_at_right_point

# Plot for right half of curve
r <- ggplot(subset_right_df, aes(x = rwibald_score_rank, y = rwibald_score)) +
  geom_point() +
  geom_vline(xintercept = rwibald_score_rank_at_right_point, color = "red", linetype = "dashed") +
  ggtitle(label= "Paleo cutoff") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5))
print(r)

# Create a column with the results
rwibald_results_key_type <- rwibald_results_all %>%
  filter(bl_comparison_tree_rwibald_elbow_key == "key") %>%
  mutate(rwibald_type = case_when(rwibald_score_rank <= rwibald_score_rank_at_left_point ~ "neo-endemic", 
                                  rwibald_score_rank >= rwibald_score_rank_at_right_point ~ "paleo-endemic",
                                  TRUE ~ "meso-endemic")) %>%
  select(NAME, rwibald_type)

# Convert the results list to a dataframe for gt
summary_results_df <- as.data.frame(t(sapply(results, c)))
results_gt <- gt(data = summary_results_df)

# Display the table
results_gt %>%
  tab_header(title = "Summary of Records and Cutoffs") %>%
  fmt_number(columns = everything(), decimals = 0) %>%
  tab_style(style = cell_text(align = "center"),
            locations = cells_body()
            )

# View(rwibald_results_key_type)

# Merge the data
rwibald_results_all <- merge(rwibald_results_all, rwibald_results_key_type[, c("NAME","rwibald_type")], by=c('NAME','NAME'),all.x=T)

# View(rwibald_results_all)
output_dir <- "quarto_outputs/"

# Write data to file
write.csv(rwibald_results_all,paste0(output_dir, "Acacia_RWiBaLD_results_all.csv") ,row.names=FALSE)

Summary of Records and Cutoffs
Negative_Records	Neo_Cutoff	Positive_Records	Paleo_Cutoff
686	71	330	991

Load RWiBaLD results & tree data by cells

Now we load the data for the RWiBaLD results and the range weighted tree data in tabular format and then replace the counts in the matrix with the branch lengths for that group so we can generate the histograms for each grid cell location. We also add the CANAPE scores to the data table for comparison and calculate the ranges of each branch.

data_dir <- "Acacia_biodiverse_exports/"
output_dir <- "quarto_outputs/"

# Data with internal branches (spatial anaylsis from BD) x grid cells
all_tree_data_csv <- paste0(data_dir, "Acacia_PD_Included_Node_List.csv")
RWiBaLD_results_csv  <- paste0(output_dir, "Acacia_RWiBaLD_results_all.csv")

all_tree_data <- read.table(all_tree_data_csv, header=T, sep=",", check.names = FALSE )
RWiBaLD_results <- read.table(RWiBaLD_results_csv, header=T, sep=",")

#View(all_tree_data)
#View(RWiBaLD_results)
valueToUse <- "rwibald_score"

# Iterate over the columns in Table 1
for (col in colnames(all_tree_data)[-c(1:3)]) {
  # get the corresponding "valueToUse" column specified above from Table 2
  branch_length <- RWiBaLD_results[RWiBaLD_results$NAME == col, paste0(valueToUse)]
  # replace the numbers in Table 1 with the "valueToUse" value
  all_tree_data[, col] <- ifelse(is.na(all_tree_data[, col]), NA, as.numeric(branch_length))
}

# View(all_tree_data)

# Write data to file
write.csv(all_tree_data, paste0(output_dir, "Acacia_PD_Included_Node_List_", valueToUse, ".csv"), row.names=FALSE)

# Get CANAPE data from biodiverse as well
canape_csv <- paste0(data_dir, "Acacia_Rand1_CANAPE_Export.csv")
canape_results <- read.table(canape_csv, header=T,sep=",")

# View(canape_results)

canape_group_data <- canape_results %>%
                     left_join(all_tree_data %>%
                               select(-c(Axis_0, Axis_1)), by = "ELEMENT"
                               )

# Transpose the dataframe
data <- t(canape_group_data)

# View(data)

# Set the first row as column names
transposed_df <- setNames(data.frame(data[-1,]), data[1,])

# View(transposed_df)

# Merge CANAPE and RWiBaLD data
canape_group_data_rwibald <- transposed_df %>%
  mutate(range_cell_count = rowSums(!is.na(.))) %>%
  tibble::rownames_to_column(var = "NAME") %>%
  left_join(select(rwibald_results_all, NAME, rwibald_type), by = 'NAME') %>%
  mutate(rwibald_type = ifelse(is.na(rwibald_type), "other", rwibald_type)) 

# Set row names
rownames(canape_group_data_rwibald) <- canape_group_data_rwibald$NAME 

# View(canape_group_data_rwibald)

# # Add range data to results
# rwibald_results_all_with_range <- rwibald_results_all %>%
#   left_join(select(canape_group_data_rwibald, NAME, range_cell_count),by = "NAME") %>%
#   mutate(rwibald_type = replace(rwibald_type,is.na(rwibald_type),"other"))

# Add range data to results and create three new columns
rwibald_results_all_with_range <- rwibald_results_all %>%
  left_join(select(canape_group_data_rwibald, NAME, range_cell_count), by = "NAME") %>%
  mutate(rwibald_type = replace(rwibald_type, is.na(rwibald_type), "other"),
         non_range_weighted_branch_length_observed_tree = branch_length_observed_tree * range_cell_count,
         non_range_weighted_branch_length_comparison_tree = branch_length_comparison_tree * range_cell_count,
         diff_non_range_weighted_observed_to_comparison_bl = non_range_weighted_branch_length_observed_tree - non_range_weighted_branch_length_comparison_tree
        ) %>%
  mutate(rank_BaLD = rank(diff_non_range_weighted_observed_to_comparison_bl, ties.method = "first"))


# Write data to file
write.csv(rwibald_results_all_with_range, paste0(output_dir, "Acacia_RWiBaLD_results_all_with_range.csv"), row.names=FALSE)

# View(rwibald_results_all_with_range)
# View(rwibald_results_all)

Generate figure 2B - ranked RWiBaLD score by RWiBaLD score

Here we plot the ranked RWiBaLD score (x) by the RWiBaLD score (y) (Figure 2B), colouring the different RWiBaLD Categories: neo-endemic (red), meso-endemic (darkolivegreen4) and paleo-endemic (royalblue1).

# Find the neo_cutoff
neo_cutoff <- rwibald_results_all_with_range %>%
  filter(rwibald_type == "neo-endemic") %>%
  summarize(highest_rank = max(rwibald_score_rank, na.rm = TRUE)) %>%
  pull(highest_rank)

# Find the paleo_cutoff
paleo_cutoff <- rwibald_results_all_with_range %>%
  filter(rwibald_type == "paleo-endemic") %>%
  summarize(lowest_rank = min(rwibald_score_rank, na.rm = TRUE)) %>%
  pull(lowest_rank)

# Get only the rwibald branches of key interest
rwibald_results_all_key_only <- rwibald_results_all_with_range %>% 
                                filter(bl_comparison_tree_rwibald_elbow_key == "key")

# View(rwibald_results_all_key_only)
# View(rwibald_results_all_with_range)

# Plot rwibald rank X rwibald score
rwibald_key_plot <- ggplot() +
    geom_point(data=rwibald_results_all_with_range, aes(x = rwibald_score_rank, y = rwibald_score), fill="transparent", size = 3, colour= "grey77", alpha = 0.4, pch = 21) +
  geom_point(data = rwibald_results_all_key_only, aes(x = rwibald_score_rank, y = rwibald_score, fill = rwibald_type, color = rwibald_type), size = 3, alpha = 0.9, pch = 21) +
  scale_fill_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue1", "neo-endemic" = "red", "meso-endemic" = "darkolivegreen4"), labels = c("neo-endemic","meso-endemic", "paleo-endemic"), limits = c("neo-endemic","meso-endemic", "paleo-endemic"),  na.value = "transparent") +
  scale_color_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue4", "neo-endemic" = "red4", "meso-endemic" = "darkolivegreen"), labels = c("neo-endemic", "meso-endemic", "paleo-endemic"), limits = c("neo-endemic", "meso-endemic", "paleo-endemic"), na.value = "transparent") +
  geom_vline(xintercept = neo_cutoff, color = "red", linetype = "dashed") +
  geom_vline(xintercept = paleo_cutoff, color = "red", linetype = "dashed") +
  xlab("RWiBaLD Score Rank (shortest-longest)") +
  ylab("Range Weighted Branch\nLength Difference (RWiBaLD) Score") +
 #annotate("segment", x = results$Negative_Records, xend = results$Negative_Records, 
  #          y = max(rwibald_results_all_with_range$rwibald_score) * 1.1, yend = 0, 
  #          arrow = arrow(length = unit(0.2, "cm")), color = "grey74") +
  geom_vline(xintercept = results$Negative_Records, color = "grey74", linetype = "solid") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5), legend.position="bottom", legend.text = element_text(size = 12))

print(rwibald_key_plot)

ggsave("quarto_outputs/figures/Figure2_B.png", plot = rwibald_key_plot,  scale = 1,
        width = 2048,
        height = 1024,
        units = "px",
        dpi = 300)

Generate figure 2C - ranked BaLD by BaLD (non range weighted)

Illustrating the distribution of branch lengths on the observed tree with no range-weighting; the Y-axis is the difference between length on the observed tree and length on the comparison tree for each branch; the X-axis is ranked branch length difference (shortest - longest).  The branches of interest are colored by RWiBaLD category as in 2B

#View(rwibald_results_all_with_range)

#View(rwibald_results_all_key_only)

# Plot non_range_weighted score
rwibald_key_plot_non_range_weighted <- ggplot() +
    #geom_point(data=rwibald_results_all_with_range, aes(x = reorder(non_range_weighted_branch_length_observed_tree, non_range_weighted_branch_length_observed_tree), y = diff_non_range_weighted_observed_to_comparison_bl), fill="transparent", size = 3, colour= "grey77", alpha = 0.4, pch = 21) +
  geom_point(data=rwibald_results_all_with_range, aes(x = rank_BaLD, y = diff_non_range_weighted_observed_to_comparison_bl), fill="transparent", size = 3, colour= "grey77", alpha = 0.4, pch = 21) +
  geom_point(data = rwibald_results_all_key_only, aes(x =  rank_BaLD, y = diff_non_range_weighted_observed_to_comparison_bl, fill = rwibald_type, color = rwibald_type), size = 3, alpha = 0.9, pch = 21) +
  scale_fill_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue1", "neo-endemic" = "red", "meso-endemic" = "darkolivegreen4"), labels = c("neo-endemic","meso-endemic", "paleo-endemic"), limits = c("neo-endemic","meso-endemic", "paleo-endemic"),  na.value = "transparent") +
  scale_color_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue4", "neo-endemic" = "red4", "meso-endemic" = "darkolivegreen"), labels = c("neo-endemic", "meso-endemic", "paleo-endemic"), limits = c("neo-endemic", "meso-endemic", "paleo-endemic"), na.value = "transparent") +
  #geom_vline(xintercept = neo_cutoff, color = "red", linetype = "dashed") +
  #geom_vline(xintercept = paleo_cutoff, color = "red", linetype = "dashed") +
  xlab("Unweighted Branch Length Difference (BaLD) Score Rank (shortest−longest)") +
  ylab("Branch Length\nDifference (BaLD) Score") +
  geom_vline(xintercept = results$Negative_Records, color = "grey74", linetype = "solid") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5), legend.position="none", legend.text = element_text(size = 12), axis.title.x = element_text(size = 12), axis.text.x = element_text(size = 8))

print(rwibald_key_plot_non_range_weighted)

ggsave("quarto_outputs/figures/Figure2_C.png", plot = rwibald_key_plot_non_range_weighted,  scale = 1,
        width = 2048,
        height = 1024,
        units = "px",
        dpi = 300)

Generate multi-panel figure 2ABC

Here we generate a 2 up figure using patchwork

patchwork <- branch_length_by_rank / rwibald_key_plot / rwibald_key_plot_non_range_weighted + plot_annotation(
  #title = 'Branches of Interest for Range Weighted Branch Length Difference (RWiBaLD)\n&\nRWiBaLD Scores With Categories Calculated Using Elbow Statistic',
  theme = theme(plot.title = element_text(hjust = 0.5)),
  subtitle = '',
  caption = '',
tag_levels = 'A') + 
  theme(plot.tag.position = c(0, 1),
        plot.tag = element_text(size = 12, hjust = 0, vjust = 0)) 

#print(patchwork)

ggsave("quarto_outputs/figures/Figure2_ABC.png", patchwork, width = 3000, height = 3600, units = "px")

Generate Figure 3A

Here we create a plot of RWiBaLD score (x) by branch range (cells) (Figure 3A), coloured by RWiBaLD Categories: neo-endemic (red), meso-endemic (green) and paleo-endemic (blue).

# Plot RWiBaLD rank X branch range
rwibald_key_plot_range <- ggplot() +
    geom_point(data=rwibald_results_all_with_range, aes(x = rwibald_score_rank, y = range_cell_count), fill="transparent", size = 2, colour= "grey77", alpha = 0.4, pch = 21) +
  geom_point(data = rwibald_results_all_key_only, aes(x = rwibald_score_rank, y = range_cell_count, fill = rwibald_type, color = rwibald_type), size = 2, alpha = 0.9, pch = 21) +
  scale_fill_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue1","neo-endemic" = "red", "meso-endemic" = "darkolivegreen4"), labels = c("neo-endemic","meso-endemic", "paleo-endemic"), limits = c("neo-endemic","meso-endemic", "paleo-endemic"),  na.value = "transparent") +
  scale_color_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue4", "neo-endemic" = "red4", "meso-endemic" = "darkolivegreen"), labels = c("neo-endemic", "meso-endemic", "paleo-endemic"), limits = c("neo-endemic", "meso-endemic", "paleo-endemic"), na.value = "transparent") +
  geom_vline(xintercept = neo_cutoff, color = "red", linetype = "dashed") +
  geom_vline(xintercept = paleo_cutoff, color = "red", linetype = "dashed") +
  geom_vline(xintercept = results$Negative_Records, color = "grey74", linetype = "solid") +
  xlab("RWiBaLD Score Rank (shortest-longest)") +
  ylab("Branch Range (cells)") +
  theme_classic() +
  theme(plot.title = element_text(hjust =1),
        legend.position="none", 
        legend.text = element_text(size = rel(1)),
        legend.title = element_text(size = rel(1)), 
        axis.title.x = element_text(size = rel(1)),
        axis.title.y = element_text(size = rel(1)),
        axis.text.x = element_text(size = rel(1)),
        axis.text.y = element_text(size = rel(1)))

print(rwibald_key_plot_range)

ggsave("quarto_outputs/figures/Figure3_A.png", plot = rwibald_key_plot_range,  scale = 1,
        width = 2048,
        height = 1024,
        units = "px",
        dpi = 300)

Generate Figure 3B

Now the same data as figure 3A but first removing all internal branches before plotting the data. Note: the neo / meso /paleo cuttofs are calculated on the whole dataset and the filtering to terminals done after that.

# For plotting purposes get only the rwibald branches of key interest and terminals only
rwibald_results_all_key_only_terminals <- rwibald_results_all_with_range %>%
  filter(!grepl("^[0-9]", NAME)) %>%
  mutate(rwibald_score_rank_terminals = rank(rwibald_score, ties.method = "first")) %>% 
    filter(bl_comparison_tree_rwibald_elbow_key == "key") 

rwibald_results_all_with_range_terminals <- rwibald_results_all_with_range %>% 
    filter(!grepl("^[0-9]", NAME)) %>%
  mutate(rwibald_score_rank_terminals = rank(rwibald_score, ties.method = "first"))

# Find the neo_cutoff
neo_cutoff_terms <- rwibald_results_all_with_range_terminals %>%
  filter(rwibald_type == "neo-endemic") %>%
  summarize(highest_rank = max(rwibald_score_rank_terminals, na.rm = TRUE)) %>%
  pull(highest_rank)

# Find the paleo_cutoff
paleo_cutoff_terms <- rwibald_results_all_with_range_terminals %>%
  filter(rwibald_type == "paleo-endemic") %>%
  summarize(lowest_rank = min(rwibald_score_rank_terminals, na.rm = TRUE)) %>%
  pull(lowest_rank)

# Subset the left and right dataframes
subset_left_df_terms <- rwibald_results_all_with_range_terminals[rwibald_results_all_with_range_terminals$rwibald_score <= 0, ]

# Records in negative data
negative_records_terms <- nrow(subset_left_df_terms)

# View(rwibald_results_all_key_only_terminals)
# View(rwibald_results_all_with_range_terminals)

# View(rwibald_results_all_with_range)

# Plot rwibald rank x rwibald score
# rwibald_key_plot_terminals <- ggplot() +
#     geom_point(data=rwibald_results_all_with_range_terminals, aes(x = rwibald_score_rank_terminals, y = rwibald_score), fill="transparent", size =3, colour= "grey77", alpha = 0.4, pch=21) +
#   geom_point(data = rwibald_results_all_key_only_terminals, aes(x = rwibald_score_rank_terminals, y = rwibald_score, fill = rwibald_type, color = rwibald_type), size =3, alpha = 0.9, pch=21) +
#   scale_fill_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue1", "neo-endemic" = "red", "meso-endemic" = "darkolivegreen4"), labels = c("neo-endemic","meso-endemic", "paleo-endemic"), limits = c("neo-endemic","meso-endemic", "paleo-endemic"),  na.value = "transparent") +
#   scale_color_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue4", "neo-endemic" = "red4", "meso-endemic" = "darkolivegreen"), labels = c("neo-endemic", "meso-endemic", "paleo-endemic"), limits = c("neo-endemic", "meso-endemic", "paleo-endemic"), na.value = "transparent") +
#   geom_vline(xintercept = neo_cutoff, color = "red", linetype = "dashed") +
#   geom_vline(xintercept = paleo_cutoff, color = "red", linetype = "dashed") +
#   xlab("RWiBaLD Score Rank Terminals Only (shortest-longest)") +
#   ylab("Range Weighted Branch\nLength Difference (RWiBaLD) Score") +
#   theme_classic() +
#   theme(plot.title = element_text(hjust = 0.5), legend.position="bottom", legend.text = element_text(size = 12))
# 
# print(rwibald_key_plot_terminals)
# 
# ggsave("quarto_outputs/figures/Figure3_B_terminals_only.png", plot = rwibald_key_plot_terminals,  scale = 1,
#         width = 2048,
#         height = 1024,
#         units = "px",
#         dpi = 300)

# Plot rwibald rank x branch range
rwibald_key_plot_range_terminals <- ggplot() +
    geom_point(data=rwibald_results_all_with_range_terminals, aes(x = rwibald_score_rank_terminals, y = range_cell_count), fill="transparent", size =3, colour= "grey77", alpha = 0.4, pch=21) +
  geom_point(data = rwibald_results_all_key_only_terminals, aes(x = rwibald_score_rank_terminals, y = range_cell_count, fill = rwibald_type, color = rwibald_type), size =3, alpha = 0.9, pch=21) +
  scale_fill_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue1","neo-endemic" = "red", "meso-endemic" = "darkolivegreen4"), labels = c("neo-endemic","meso-endemic", "paleo-endemic"), limits = c("neo-endemic","meso-endemic", "paleo-endemic"),  na.value = "transparent") +
  scale_color_manual(name = "RWiBaLD Category", values = c("paleo-endemic" = "royalblue4", "neo-endemic" = "red4", "meso-endemic" = "darkolivegreen"), labels = c("neo-endemic", "meso-endemic", "paleo-endemic"), limits = c("neo-endemic", "meso-endemic", "paleo-endemic"), na.value = "transparent") +
  geom_vline(xintercept = neo_cutoff_terms, color = "red", linetype = "dashed") +
  geom_vline(xintercept = paleo_cutoff_terms, color = "red", linetype = "dashed") +
    geom_vline(xintercept = negative_records_terms, color = "grey74", linetype = "solid") +
  xlab("RWiBaLD Score Rank Terminals Only (shortest-longest)") +
  ylab("Branch Range (cells)") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5), legend.position="bottom", legend.text = element_text(size=rel(1)))

print(rwibald_key_plot_range_terminals)

ggsave("quarto_outputs/figures/Figure3_B.png", plot = rwibald_key_plot_range_terminals,  scale = 1,
        width = 2048,
        height = 1024,
        units = "px",
        dpi = 300)

Generate multi-panel figure 3AB

patchwork <- rwibald_key_plot_range / rwibald_key_plot_range_terminals + plot_annotation(
  theme = theme(plot.title = element_text(hjust = 0.5)),
  subtitle = '',
  caption = '',
tag_levels = 'A') + 
   theme(plot.tag.position = c(0, 1),
        plot.tag = element_text(size = 12, hjust = 0, vjust = 0)) 

#print(patchwork)

ggsave("quarto_outputs/figures/Figure3_AB.png", patchwork, width = 3000, height = 3600, units = "px")

Read tree file & generate supplementary figure 1

Now we load the range weighted tree file exported from biodiverse and colour code it by the RWiBaLD categories.

# if need be check and install ggtree using this code
# if (!require("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
#  
# BiocManager::install("ggtree")

data_dir <- "Acacia_biodiverse_exports/"

all_tree_data_nwk <- paste0(data_dir, "acacia_tree_TRIMMED1_EQ1_RW1.nwk")

myTree <- read.tree(file=all_tree_data_nwk)

rwibald_results_all <- rwibald_results_all %>% 
  mutate(rwibald_type = replace(rwibald_type,is.na(rwibald_type),"other"))

# View(rwibald_results_all)

rwibald_results_all$tree_cols <- as.factor(rwibald_results_all$rwibald_type)

# Remove quotes from labels so they match dataframe
myTree$tip.label <- gsub("'","",myTree$tip.label)
myTree$node.label <- gsub("'","",myTree$node.label)

# Create ggtree object
g <- ggtree(myTree) %<+% rwibald_results_all +
      aes(color=tree_cols) +
      scale_color_manual(values = c("paleo-endemic" = "royalblue1",
                                 "neo-endemic" = "red",
                                 "meso-endemic" = "darkolivegreen")) +
     # geom_text2(aes(label = node), 
     #         hjust =0,    # adjust horizontal position
     #         vjust = 0,     # adjust vertical position
     #         size = 1,        # adjust text size
     #         color = "blue")  + # adjust text color
      theme(legend.position="none") +
       geom_tiplab(as_ylab=FALSE, size=1)

# Display the plot
#print(g)

ggsave("quarto_outputs/figures/SupFigure1_full_tree.png", g, width = 2048,
        height = 4024, units = "px")

Generate tree in 2 halves supplementary figure 1A and 1B

data_dir <- "Acacia_biodiverse_exports/"
all_tree_data_nwk <- paste0(data_dir, "acacia_tree_TRIMMED1_EQ1_RW1.nwk")

myTree <- read.tree(file=all_tree_data_nwk)

rwibald_results_all <- rwibald_results_all %>% 
  mutate(rwibald_type = replace(rwibald_type, is.na(rwibald_type), "other"))

rwibald_results_all$tree_cols <- as.factor(rwibald_results_all$rwibald_type)

# Remove quotes from labels so they match the dataframe
myTree$tip.label <- gsub("'", "", myTree$tip.label)
myTree$node.label <- gsub("'", "", myTree$node.label)

# Calculate the halfway point
half <- length(myTree$tip.label) / 2

# Sort the tip labels for consistency
tip_labels <- myTree$tip.label
first_half_tips <- tip_labels[1:half]
second_half_tips <- tip_labels[(half + 1):length(tip_labels)]

# Identify MRCA nodes for each half
node_first_half <- getMRCA(myTree, first_half_tips)
node_second_half <- getMRCA(myTree, second_half_tips)


# Create the base ggtree object
g <- ggtree(myTree) %<+% rwibald_results_all +
      aes(color = tree_cols) +
      scale_color_manual(values = c("paleo-endemic" = "royalblue1",
                                    "neo-endemic" = "red",
                                    "meso-endemic" = "darkolivegreen",
                                    "other" = "gray66")) +
      theme(legend.position = "none") +
      geom_tiplab(as_ylab = FALSE, size=1)

#plot(g)

# Identify MRCA node
mrca_node <- MRCA(g, second_half_tips)

second_half_tree <- viewClade(tree_view = g, mrca_node) +
    geom_point2(
      aes(subset = (node == mrca_node)),
    shape = 21,
    size = 2,
    fill = 'gray26',
    nudge_x = 20,
    position = position_nudge(y = -88)) 


# +
#   theme(panel.ontop = FALSE,
#          panel.spacing.x = unit(c(1, 1, 1, 1), "cm"),
#          panel.background = element_rect(color = "green", fill="transparent", linewidth = 10),
#          plot.margin = unit(c(1, 1, 1, 1), "cm"))


#plot(second_half_tree)
 
# Save the figure showing the second half expanded and the first half collapsed
ggsave("quarto_outputs/figures/SupFigure_1A.png", 
        second_half_tree, width = 2048, height = 4024, units = "px")

#plot(g)
# Collapse the second half of the tree
g_half_collapsed <- collapse(g, node = node_second_half) +
  # Add a point to indicate the collapsed node for reference
  geom_point2(aes(subset=(node == node_second_half)), shape=21, size=2, fill='gray26')

#plot(g_half_collapsed)

# Save the figure showing the first half expanded and the second half collapsed
ggsave("quarto_outputs/figures/SupFigure_1B.png", 
       g_half_collapsed, width = 2048, height = 4024, units = "px")

Load data & generate histograms for all cells

Next we load the data and generate histograms. This code generates a histogram for every cell in the dataset. For convenience the file names contain the CANAPE code and whether the histogram contains branches of key interest.

#function to change decimal places displayed on plot
scaleFUN <- function(x) sprintf("%.8g", x)

# Function to replace '-' with 'm' and ':' with '_'
clean_column_names <- function(df) {
  new_names <- colnames(df) %>%
    gsub("-", "neg", .) %>%
    gsub(":", "_", .)
  colnames(df) <- new_names
  return(df)
}

# Clean column names using the function
canape_group_data_rwibald <- clean_column_names(canape_group_data_rwibald)

#View(canape_group_data_rwibald)

col_scheme <- c("paleo-endemic" = "royalblue1","neo-endemic" = "red", "meso-endemic" = "darkolivegreen", "other" = "lightgoldenrodyellow")
#legend_labels <- c("neo-endemic"="Neo Endemic","paleo-endemic"="paleo Endemic", "other" = "other", "meso-endemic" = "Meso Endemic")

#data <- transposed_df
#View(canape_key_group_data_rwibald)

#Generate all histograms
for (i in 2:(ncol(canape_group_data_rwibald)-2)) {# skip first column and last 2 as they are NAME,cell_count,rwibald_type
#for (i in 74:74) {
  #i <- 74
  data <- canape_group_data_rwibald[-(1:6),] # remove the rows not needed
  col_name <- colnames(data)[i] # get the column name to generate the histogram of
  colourByCol <- "rwibald_type"  # set the column to use for colouring the histogram
  numberOfBins <- 41 # set number of bins to use 
  hist_data <- na.omit(as.numeric(data[[col_name]])) # extract that column data for ploting
  hist_range <- range(hist_data,na.rm=TRUE) # calculate the range of all values in the column ie. min/max value

#calculate the total range of the whole data set to get a consistent x scale on all histograms.    
dataForRange <- data %>%
  select(-first(colnames(.)), -last(colnames(.))) %>%
  mutate(across(everything(), as.numeric)) 
totalRange <- range(dataForRange, na.rm=TRUE)#lowest and highedt values in the dataset
totalRangeGap <- max(dataForRange, na.rm = TRUE) - min(dataForRange, na.rm = TRUE) # distance between range above

  range <- max(hist_data) - min(hist_data)
  binSize <- range/numberOfBins
  farthest_number <- max(abs(hist_range)) # calculate the number farthest from 0
  x_min <- -(farthest_number)-2*(binSize) # set xmin so zero is centred
  x_max <- (farthest_number)+2*(binSize) # set xmax so zero is centred
 
  #check to see if any of the branches in the histogram are RWIBALD significant to add to the file names
  result <- data %>%
  select(!!sym(col_name), !!sym(colourByCol)) %>%  
  filter(!is.na(!!sym(col_name))) %>%
  summarise(result = ifelse(any(!!sym(colourByCol) %in% c("paleo-endemic", "neo-endemic", "meso-endemic")), "key", "zero-key")) %>%
  pull(result)
  
  #View(result)
  # setup the file names 
  CANAPE_CODE <- canape_group_data_rwibald["CANAPE_CODE", col_name]
  cell <- str_replace_all(col_name, "[:]", "_")
  filename <- paste0("quarto_outputs/histograms/", cell, "_CC_", CANAPE_CODE,"_", result, "_", numberOfBins,"bins.png")
  
  #print(filename)
  # get a subset of dataframe for the histogram
  plot_data <- data %>%
  select(!!sym(col_name), !!sym(colourByCol)) %>%  
  filter(!is.na(!!sym(col_name))) %>%
  arrange(!!sym(colourByCol))
  
  #View(plot_data)

 plot <- ggplot(plot_data, aes(x = as.numeric(.data[[col_name]]), fill = forcats::fct_rev(rwibald_type))) +
  geom_histogram(color = "black", linewidth= 0.2, bins = numberOfBins) +
  scale_fill_manual(values = col_scheme) +
  xlim(x_min, x_max) +
  coord_cartesian(ylim = c(0, 20)) +
  labs(x = "RWiBaLD Score", y = "Frequency", fill = "Branch Category") +
  theme_bw()
 
 plot <- plot +
  stat_bin(
    aes(label = after_stat(if_else (condition = count>20, as.character(count), ""))),
    bins= as.numeric(numberOfBins),
    position=position_stack(vjust=0.1),
    pad=TRUE,
    geom = "text",
    color = "black",
    size = 2,
    y = 19
  )
 
 CairoPNG(width = 1024, height = 600, file = filename, canvas="white", bg = "white", units="px", dpi=96, title = "") 
  print(plot)
 dev.off()
}

Load data & generate 12 histograms for figure 4A-L

This code filters out only the 12 histograms included in Figure 4 and generates the ggplot2 objects of them.

data <- t(canape_group_data)
#View(data)

#Transpose the dataframe and set the first row as column names
transposed_df <- setNames(data.frame(data[-1,]), data[1,])

#View(transposed_df)

#cells to keep
cols_to_keep <- c("NAME", "-1825000:-2975000", "75000:-2525000", "625000:-3475000","-1425000:-3225000","1775000:-3725000", "-1425000:-3475000", "-1225000:-3775000", "-1425000:-3275000", "1225000:-1275000", "-1175000:-3275000", "-1075000:-2825000", "-1075000:-2375000")

#Calculate RWiBaLD significants and Statistics etc.
canape_group_data_rwibald <- transposed_df %>%
  tibble::rownames_to_column(var = "NAME") %>%
  select(all_of(cols_to_keep)) %>%
  left_join(select(rwibald_results_all, NAME, rwibald_type), by = 'NAME') %>%
  mutate(rwibald_type = ifelse(is.na(rwibald_type), "other", rwibald_type))   

# Check if the column is already a factor
 if (!is.factor(canape_group_data_rwibald$rwibald_type)) {
   # If not a factor, convert and specify levels
   canape_group_data_rwibald$rwibald_type <- factor(canape_group_data_rwibald$rwibald_type, levels = c("neo-endemic", "meso-endemic", "paleo-endemic", "other"))
 }

#View(canape_group_data_rwibald)

# Function to replace '-' with 'm' and ':' with '_'
clean_column_names <- function(df) {
  new_names <- colnames(df) %>%
    gsub("-", "neg", .) %>%
    gsub(":", "_", .)
  colnames(df) <- new_names
  return(df)
}

# Clean column names using the function
canape_group_data_rwibald <- clean_column_names(canape_group_data_rwibald)

rownames(canape_group_data_rwibald) <- canape_group_data_rwibald$NAME 

#View(canape_group_data_rwibald)

# Write data to file
write.csv(canape_group_data_rwibald, paste0("quarto_outputs/transposed_df_figure_only.csv"), row.names=FALSE)

#function to change decimal places displayed on plot
scaleFUN <- function(x) sprintf("%.8g", x)

#View(canape_group_data_rwibald)

col_scheme <- c("paleo-endemic" = "royalblue1",
                "neo-endemic" = "red",
                "meso-endemic" = "darkolivegreen",
                "other" = "lightgoldenrodyellow"
                )

legend_labels <- c("neo-endemic" = "neo-endemic",
                   "paleo-endemic" = "paleo-endemic",
                   "meso-endemic" = "meso-endemic",
                   "other" = "other"
                   )

legend_order <- c("neo-endemic", "meso-endemic", "paleo-endemic", "other")

#data <- transposed_df
#View(canape_group_data_rwibald)

# Create an empty list to store plots
plot_list <- list()

#Generate histograms
for (i in 2:(ncol(canape_group_data_rwibald)-1)) {# skip first column and last 2 as they are NAME,rwibald_type
  #i <- 2
  local({ # have to make this local to allow multiple plots in patchwork
    i <- i
  data <- canape_group_data_rwibald[-(1:6),] # remove the rows not needed
  col_name <- colnames(data)[i] # get the column name to generate the histogram of
  colourByCol <- "rwibald_type"  # set the column to use for colouring the histogram
  numberOfBins <- 41 # set number of bins to use 
  
  # have to show only one legend as the 'collect' feature in patchwork does not collect the legends as desired
  if(i == 3){
  showLegend <- TRUE
  } else {
  showLegend <- FALSE
  }
 
  hist_data <- na.omit(as.numeric(data[[col_name]])) # extract that column data for ploting
  hist_range <- range(hist_data,na.rm=TRUE) # calculate the range of all values in the column ie. min/max value

  #calculate the total range of the whole data set to get a consistent x scale on all histograms.    
  dataForRange <- data %>%
  select(-first(colnames(.)), -last(colnames(.))) %>%
  mutate(across(everything(), as.numeric)) 
  totalRange <- range(dataForRange, na.rm=TRUE)#lowest and highest values in the dataset
  totalRangeGap <- max(dataForRange, na.rm = TRUE) - min(dataForRange, na.rm = TRUE) # distance between range above

  range <- max(hist_data) - min(hist_data)
  binSize <- range/numberOfBins
  farthest_number <- max(abs(hist_range)) # calculate the number farthest from 0
  x_min <- -(farthest_number)-2*(binSize) # set xmin so zero is centred
  x_max <- (farthest_number)+2*(binSize) # set xmax so zero is centred
 
  # get a subset of dataframe for the histogram
  plot_data <- data %>%
  mutate(!!sym(col_name) := as.numeric(!!sym(col_name))) %>% 
  select(!!sym(col_name), !!sym(colourByCol)) %>%  
  filter(!is.na(!!sym(col_name))) %>%
  arrange(!!sym(colourByCol))
  
  #View(plot_data)
  #str(plot_data)
 
  plot <- ggplot(plot_data, aes(x = .data[[col_name]], fill = forcats::fct_rev(rwibald_type))) +
  geom_histogram(color = "black", linewidth = 0.2, bins = numberOfBins,  show.legend = showLegend) +
  scale_fill_manual(values = col_scheme, labels = legend_labels, breaks = legend_order, drop = FALSE, na.value = "transparent") +
  #xlim(x_min, x_max) +
  xlim(-0.011, 0.011) +  
  coord_cartesian(ylim = c(0, 20)) +
  labs(x = "RWiBaLD Score", y = "Frequency", fill = "RWiBaLD Category") +
  theme_bw() 

 plot <- plot +
  geom_rect(
    aes(xmin = -0.0005, xmax = 0.0005, ymin = 18.5, ymax = 19.5),
    color = "white",
    fill = "white"
  ) + 
   stat_bin(
    aes(label = after_stat(if_else (condition = count>20, as.character(count), ""))),
    bins= as.numeric(numberOfBins),
    position=position_stack(vjust=0.1),
    pad=TRUE,
    geom = "text",
    color = "black",
    size = 3,
    y = 19
  ) 

  #print(plot)
 
  # Dynamic variable name for the plot
  plot_name <- paste0("plot_", i - 1)  # Subtracting 1 to start numbering from 1
  assign(plot_name, plot)

  # Add the plot to the list
  plot_list[[i - 1]] <<- get(plot_name)
  #rm(plot)
  })
 
}

 #print(plot_list[[1]])

#save the plotlist for later on
saveRDS(plot_list, "quarto_outputs/plotlist.rds")

Generate multi-panel histogram figure 4A-L

This code compiles all 12 histograms generated above into one figure.

plot_list <- readRDS("quarto_outputs/plotlist.rds")

patchwork <-  plot_list[[1]] + xlab(NULL) + 
              guides(x = "none") + 
              plot_list[[2]] + xlab(NULL) + ylab(NULL) + 
              guides(x = "none", y = "none") + 
              plot_list[[3]] + xlab(NULL) + ylab(NULL) + 
              guides(x = "none", y = "none") +   
              plot_list[[4]] + xlab(NULL) + ylab(NULL) + 
              guides(x = "none", y = "none") + 
              plot_list[[5]] + xlab(NULL) + guides(x = "none") + 
              plot_list[[6]] + xlab(NULL) + ylab(NULL) + 
              guides(x = "none", y = "none") + 
              plot_list[[7]] + xlab(NULL) + ylab(NULL) + 
              guides(x = "none", y = "none") + 
              plot_list[[8]] + xlab(NULL) + ylab(NULL) + 
              guides(x = "none", y = "none") + 
              plot_list[[9]] +  
              plot_list[[10]] + ylab(NULL) + guides(y = "none") +
              plot_list[[11]] + ylab(NULL) + guides(y = "none") +
              plot_list[[12]] + ylab(NULL) + guides(y = "none") +
              plot_annotation(
              subtitle = '                     CANAPE-Neo                                CANAPE-paleo                                                       CANAPE-Mixed', 
              caption = '',
              tag_levels = 'A') +
              plot_layout(ncol = 4, guides = 'collect') &
              theme(legend.position = "bottom", plot.tag.position = c(0.9, 0.9),
                    plot.tag = element_text(size = 12, hjust = 0, vjust = 0))
              
#print(patchwork) 

ggsave("quarto_outputs/figures/Figure4_A.png", patchwork, width = 3000, height = 2400, units = "px")

Load results data & generate data files for biome maps

Now we take the RWiBaLD results and split the taxa in the original specimen data from the Categorical Analysis of Neo And Paleo-Endemisim (CANAPE) paper according to these RWiBaLD categories so we can overlay them on the biome map published by Crisp et al. in 2004.

original_paper_specimen_data_file <- "doi_10_5061_dryad_dv4qk__v20150514/Point_distribution_Australian_Phylogenetic_Diversity_Acacia.csv"

original_paper_specimen_data <- read.table(original_paper_specimen_data_file, header=T, sep=",")

#View(original_paper_specimen_data)
#View(rwibald_results_all_with_range)

# Summarize the data in the rwibaled_type column
summary_df <- rwibald_results_all_with_range %>%
  group_by(rwibald_type) %>%
  summarise(count = n())  # Count of each rwibaled_type

# Create a table using gt
summary_table <- summary_df %>%
  gt() %>%
  tab_header(
    title = "Summary of rwibaled_type",
    subtitle = "Count of each type"
  )

# Print the table
summary_table

# Extract the 'NAME' values where 'rwibald_type' is 'meso-endemic'
meso_names <- rwibald_results_all_with_range[rwibald_results_all_with_range$rwibald_type == "meso-endemic", "NAME"]

# Filter the 'original_paper_specimen_data' dataframe
meso_taxa <- original_paper_specimen_data[original_paper_specimen_data$Species %in% meso_names, ]

# Write data to file
write.csv(meso_taxa, "quarto_outputs/Meso_endemics.csv" ,row.names=FALSE)

# Extract the 'NAME' values where 'rwibald_type' is 'neo-endemic'
neo_names <- rwibald_results_all_with_range[rwibald_results_all_with_range$rwibald_type == "neo-endemic", "NAME"]

# Filter the 'original_paper_specimen_data' dataframe
neo_taxa <- original_paper_specimen_data[original_paper_specimen_data$Species %in% neo_names, ]

# Write data to file
write.csv(neo_taxa, "quarto_outputs/Neo_endemics.csv" ,row.names=FALSE)

# Extract the 'NAME' values where 'rwibald_type' is 'paleo-endemic'
paleo_names <- rwibald_results_all_with_range[rwibald_results_all_with_range$rwibald_type == "paleo-endemic", "NAME"]

# Filter the 'original_paper_specimen_data' dataframe
paleo_taxa <- original_paper_specimen_data[original_paper_specimen_data$Species %in% paleo_names, ]

# Write data to file
write.csv(paleo_taxa, "quarto_outputs/Paleo_endemics.csv" ,row.names=FALSE)

Summary of rwibaled_type
Count of each type
rwibald_type	count
meso-endemic	59
neo-endemic	55
other	880
paleo-endemic	21

Two tailed relative phylogenetic diversity (RPD) & CANAPE functions

This is the function for calculating Categorical Analysis of Neo- and Paleo-endemism (CANAPE) from ranked P scores of Biodiverse analysis.

#Standard 2 tailed test for RPD
significance_fun <- function(x){
  if (x >= 0.99) {
    return("Very Highly Sig")
  } else if (x >= 0.975){
    return ("Highly Sig")
  } else if (x <= 0.01){
    return ("Very Sig Low")
  } else if (x <= 0.025){
    return ("Sig Low")
  } else {
    return("Not Sig")
  }
}

#two pass test for RPE
# x=P_PE_WE_P, y=P_PHYLO_RPE_NULL2, z=P_PHYLO_RPE2
significance_super_fun <- function(x, y, z){
  if (x > 0.95 || y > 0.95) {
    if (z <= 0.025){
      return ("Neo")
    } else if (z >= 0.975){
      return ("paleo")
    } else if (x >= 0.99 || y >= 0.99){
      return ("Super")
    } else {
      return("Mixed")
    }
  } else {
    return("Not Sig")
  }
}

Load CANAPE & randomisation results

Here we load the re-created biodiverse results from the original paper, calculate CANAPE and re-generate the CANAPE map. Note: these results may differ slightly from the original paper as we re-ran the randomisation in biodiverse.

data_dir <- "Acacia_biodiverse_exports/"

# The CANAPE results file calculated in Biodiverse directly and exported
biodiverse_canape_results_file <- paste0(data_dir, "Acacia_Rand1_CANAPE_Export.csv")

# The spatial & randomisation results files calculated in Biodiverse and exported
biodiverse_observed_data_file  <- paste0(data_dir, "Acacia_SPATIAL_RESULTS_Export.csv")
biodiverse_rand_results_file <- paste0(data_dir, "Acacia_Rand1_SPATIAL_RESULTS_Export.csv")

biodiverse_canape_results <- read.table(biodiverse_canape_results_file, header=T,sep=",", check.names = FALSE )

biodiverse_observed_spatial_results <- read.table(biodiverse_observed_data_file, header=T,sep=",")
biodiverse_rand_spatial_results <- read.table(biodiverse_rand_results_file, header=T,sep=",")

biodiverse_results_concatenated <- cbind(biodiverse_observed_spatial_results, biodiverse_rand_spatial_results)

#View(biodiverse_results_concatenated)

###############################################
#Create new columns in dataframe and 
#populate them using the functions above
###############################################

targets <- c("PHYLO_RPD2", "PD_P", "PE_WE_P", "PD_P_per_taxon", "PHYLO_RPE2")

for (name in targets) {
  colname <- paste0("P_", name)  #  prepend the P_ since we want the proportions, saves some typing above
  new_colname = paste0(colname, "_SIG")
  trait_index <- match (colname, colnames(biodiverse_results_concatenated))
  # Apply the function to every row of column with index "trait_index"
  #  and generate a column in the dataframe showing significant cells
  if (!is.na(trait_index)) {
    biodiverse_results_concatenated[[new_colname]] <- apply (biodiverse_results_concatenated[trait_index],  MARGIN=c(1), significance_fun)
  } else {
    print (paste("Cannot find index", colname, "in data frame"))
  }
}

biodiverse_results_concatenated$P_PHYLO_RPE2_CANAPE_SIG <- sapply(
  1:nrow(biodiverse_results_concatenated),
  function(x) significance_super_fun(
    biodiverse_results_concatenated$P_PE_WE_P[x],
    biodiverse_results_concatenated$P_PHYLO_RPE_NULL2[x],
    biodiverse_results_concatenated$P_PHYLO_RPE2[x]
  )
)

#View(biodiverse_results_concatenated)

Generate CANAPE map figure 5A

Here we generate the CANAPE map, indicating cell locations from the histograms in figure 4A-L. CANAPE identifies geographic concentrations of high PE, and gives a summary classification of the type of endemism dominating in a location. The RWiBaLD histograms (figure 4A-L) identify the specific branches that contribute the most to PE in a given cell, and what type of endemism they represent.

#loadfonts()

myFont <- choose_font(c("HelvLight", "Arial", "sans"), quiet = TRUE) #load a font if available
   
map_text <- "Categorical Analysis of Neo- And Paleo- Endemism"
sigplot <- "P_PHYLO_RPE2_CANAPE_SIG"
col_scheme <- c("paleo" = "royalblue1","Not Sig" = "snow2", "Neo" = "red", "Super" = "#9D00FF", "Mixed"= "#CB7FFF")
legend_order <-c("Neo","paleo", "Not Sig", "Mixed", "Super")
legend_labels <- c("Neo"="Neo","paleo"="Paleo", "Not Sig"="Not significant", "Mixed"="Mixed", "Super"="Super")

biodiverse_results_concatenated[, sigplot] <- factor(biodiverse_results_concatenated[, sigplot], levels=legend_order)
Axis_0 <- "Axis_0"
Axis_1 <- "Axis_1"   
   
map_shape_file <- paste0("shape_files/coastline_albers.shp")
map_data   <- st_read(map_shape_file)  

cols_to_keep <- c("-1825000:-2975000", "75000:-2525000", "625000:-3475000","-1425000:-3225000","1775000:-3725000", "-1425000:-3475000", "-1225000:-3775000", "-1425000:-3275000", "1225000:-1275000", "-1175000:-3275000", "-1075000:-2825000", "-1075000:-2375000")

# Initialize empty vectors for x and y
x <- c()
y <- c()

# Split each element of cols_to_keep and append to x and y for plotting on the map
for (val in cols_to_keep) {
  parts <- strsplit(val, ":")[[1]]
  x <- c(x, as.numeric(parts[1]))
  y <- c(y, as.numeric(parts[2]))
}

# Print the result
#print(x)
#print(y)

labels <- c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L")

# Create data frame
histograms <- data.frame(x = x, y = y, label = labels)

# Plot the sf object with ggplot2, coloring by the BIOME column
map_plot_CANAPE  <- ggplot() +
  geom_tile(data=biodiverse_results_concatenated, aes_string(x=Axis_0, y=Axis_1, fill=sigplot)) +
  geom_sf(data = map_data, colour = "grey77" , fill="transparent") +
  scale_fill_manual(values = col_scheme,  labels=legend_labels, name="CANAPE", guide = guide_legend(direction = "horizontal", title.position = "bottom", title.hjust=0.5, title.vjust=0.5, label.position="bottom", label.hjust = 0.5, label.vjust = 0.1, lineheight=0.5))+
  geom_text_repel(data=histograms, aes(x = x, y = y, label = labels), fontface = "bold", size = 2.5, nudge_x = c(-200000, 0, 0, -100000, 250000, -300000, 200000, 100000, 200000, 200000, 200000, 200000), nudge_y = c(200000, 250000, 350000, 300000, -100000, 0, -200000, 200000, 200000, 200000, 200000, 200000)) +
   annotate("rect", xmin = -750000, xmax = -250000, ymin = -4500000, ymax = -4550000, fill = "black", colour = "black", alpha = 1, linewidth = 0.1) +
    annotate("rect", xmin = -250000, xmax = 250000, ymin = -4500000, ymax = -4550000, fill = "white", colour = "black", alpha = 1, linewidth = 0.1) +
    annotate("text", label = "0", x = -750000, y = -4650000, size=rel(2),  face = 'plain', family = myFont) +
    annotate("text", label = "500", x = -250000, y = -4650000, size=rel(2),  face = 'plain', family = myFont) +
    annotate("text", label = "1000", x = 250000, y = -4650000, size=rel(2),  face = 'plain', family = myFont) +
    annotate("text", label = "km", x = 500000, y = -4650000, size=rel(2),  face = 'plain', family = myFont) +
   theme(text = element_text(family = myFont),
         strip.background = element_blank(),
         title = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
         axis.line=element_blank(),axis.text.x=element_blank(),
         axis.text.y=element_blank(),axis.ticks=element_blank(),
         axis.title.x=element_blank(), axis.title.y=element_blank(),
         legend.position="none",
         legend.direction='horizontal',
         legend.text = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
         panel.grid = element_blank(),
         panel.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
         panel.border = element_blank(),
         plot.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
         plot.margin=unit(c(0,0,0,0),"line"))

print(map_plot_CANAPE)

ggsave('quarto_outputs/figures/Figure5_A.png', map_plot_CANAPE, width = 1024, height = 1024, units = "px", bg = "white")

Reading layer `coastline_albers' from data source 
  `C:\E\gitRepos\rwibald\shape_files\coastline_albers.shp' using driver `ESRI Shapefile'
Simple feature collection with 44 features and 4 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -1887741 ymin: -4840771 xmax: 2121462 ymax: -1029671
Projected CRS: GDA94 / Australian Albers

Generate biome map with neo-endemics figure 5B

Here we show the specimen data for all of the neo-endemic taxa categorised by RWiBaLD overlaid on the biome map published by Crisp et al. in 2004.

#loadfonts()

#myFont <- choose_font(c("HelvLight", "Arial", "sans"), quiet = TRUE) #load a font if available

col_scheme <- c("aseasonal wet" = "cornflowerblue",
                "eremean" = "lightyellow1",
                "monsoonal tropics" = "powderblue",
                "southeastern temperate" = "moccasin",
                "southwestern temperate"= "#fbb4ae")
legend_order <-c("aseasonal wet", 
                 "eremean", 
                 "monsoonal tropics",
                 "southeastern temperate", 
                 "southwestern temperate")
legend_labels <- c("aseasonal wet" = "aseasonal wet",
                   "eremean" = "eremean",
                   "monsoonal tropics" = "monsoonal tropics",
                   "southeastern temperate"="southeastern temperate",
                   "southwestern temperate"="southwestern temperate")

map_shape_file1 <- paste0("shape_files/coastline_albers.shp")
map_data   <- st_read(map_shape_file1)   
#geom_sf(data = map_data, colour = "grey77" , fill="transparent") +

map_shape_file <- paste0("shape_files/biomes_crisp_3577/biomes_crisp_3577.shp")
# Read the shapefile as an sf object
biomes_sf <- st_read(map_shape_file)

csv_file <- paste0("quarto_outputs/Neo_endemics.csv") 
# Read the CSV file into a data frame
csv_data <- read.csv(csv_file)
# Convert data frame to sf object
csv_sf <- st_as_sf(csv_data, coords = c("x_metres_EPSG_3577_Albers_Equal_Area", "y_metres_EPSG_3577_Albers_Equal_Area"), crs = st_crs(biomes_sf))


# Plot the sf object with ggplot2, coloring by the BIOME column
map_plot_Neo  <- ggplot() +
  geom_sf(data = biomes_sf, aes(fill = BIOME), colour = "grey77") +
  geom_sf(data = map_data, colour = "grey77" , fill="transparent") +
  geom_sf(data = csv_sf, fill= "red", color = "red4", shape = 21) +
 scale_fill_manual(values = col_scheme,  labels=legend_labels, name="Biomes", guide = guide_legend(direction = "horizontal", title.position = "bottom", title.hjust=0.5, title.vjust=0.5, label.position="bottom", label.hjust = 0.5, label.vjust = 0.1, lineheight=0.5))+
   theme(text = element_text(family = myFont),
         strip.background = element_blank(),
         title = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
         axis.line=element_blank(),axis.text.x=element_blank(),
         axis.text.y=element_blank(),axis.ticks=element_blank(),
         axis.title.x=element_blank(), axis.title.y=element_blank(),
         legend.position="none",
         legend.direction='horizontal',
         legend.text = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
         legend.key = element_rect(color = "black", size = 0),
         panel.grid = element_blank(),
         panel.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
         panel.border = element_blank(),
         plot.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
         plot.margin=unit(c(0,0,0,0),"line"))


print(map_plot_Neo)

ggsave("quarto_outputs/figures/Figure5_B.png", map_plot_Neo, width = 3000, height = 2600, units = "px")

Reading layer `coastline_albers' from data source 
  `C:\E\gitRepos\rwibald\shape_files\coastline_albers.shp' using driver `ESRI Shapefile'
Simple feature collection with 44 features and 4 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -1887741 ymin: -4840771 xmax: 2121462 ymax: -1029671
Projected CRS: GDA94 / Australian Albers
Reading layer `biomes_crisp_3577' from data source 
  `C:\E\gitRepos\rwibald\shape_files\biomes_crisp_3577\biomes_crisp_3577.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 5 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -1887414 ymin: -4840588 xmax: 2121442 ymax: -1087816
Projected CRS: GDA94 / Australian Albers

Generate biome map with meso-endemics figure 5C

Here we show the specimen data for all of the meso-endemic taxa categorised by RWiBaLD overlaid on the biome map published by Crisp et al. in 2004.

#loadfonts()

#myFont <- choose_font(c("HelvLight", "Arial", "sans"), quiet = TRUE) #load a font if available

map_text <- "Categorical Analysis of Neo- And Paleo- Endemism"

col_scheme <- c("aseasonal wet" = "cornflowerblue",
                "eremean" = "lightyellow1",
                "monsoonal tropics" = "powderblue",
                "southeastern temperate" = "moccasin",
                "southwestern temperate"= "#fbb4ae")
legend_order <-c("aseasonal wet", 
                 "eremean", 
                 "monsoonal tropics",
                 "southeastern temperate", 
                 "southwestern temperate")
legend_labels <- c("aseasonal wet" = "aseasonal wet",
                   "eremean" = "eremean",
                   "monsoonal tropics" = "monsoonal tropics",
                   "southeastern temperate"="southeastern temperate",
                   "southwestern temperate"="southwestern temperate")

map_shape_file1 <- paste0("shape_files/coastline_albers.shp")
map_data   <- st_read(map_shape_file1)   
#geom_sf(data = map_data, colour = "grey77" , fill="transparent") +

map_shape_file <- paste0("shape_files/biomes_crisp_3577/biomes_crisp_3577.shp")
# Read the shapefile as an sf object
biomes_sf <- st_read(map_shape_file)

csv_file <- paste0("quarto_outputs/Meso_endemics.csv") 
# Read the CSV file into a data frame
csv_data <- read.csv(csv_file)
# Convert data frame to sf object
csv_sf <- st_as_sf(csv_data, coords = c("x_metres_EPSG_3577_Albers_Equal_Area", "y_metres_EPSG_3577_Albers_Equal_Area"), crs = st_crs(biomes_sf))


# Plot the sf object with ggplot2, coloring by the BIOME column
map_plot_Meso  <- ggplot() +
  geom_sf(data = biomes_sf, aes(fill = BIOME), color = "grey77") +
  geom_sf(data = map_data, colour = "grey77" , fill="transparent") +
  geom_sf(data = csv_sf, fill= "forestgreen", color = "darkgreen", shape = 21) +
  scale_fill_manual(values = col_scheme,  labels=legend_labels, name="Biomes", guide = guide_legend(direction = "horizontal", title.position = "bottom", title.hjust=0.5, title.vjust=0.5, label.position="bottom", label.hjust = 0.5, label.vjust = 0.1, lineheight=0.5))+
   theme(text = element_text(family = myFont),
         strip.background = element_blank(),
         title = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
         axis.line=element_blank(),axis.text.x=element_blank(),
         axis.text.y=element_blank(),axis.ticks=element_blank(),
         axis.title.x=element_blank(), axis.title.y=element_blank(),
         legend.position="none",
         legend.direction='horizontal',
         legend.text = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
         legend.key = element_rect(color = "black", size = 0),
         panel.grid = element_blank(),
         panel.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
         panel.border = element_blank(),
         plot.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
         plot.margin=unit(c(0,0,0,0),"line"))

print(map_plot_Meso)

ggsave("quarto_outputs/figures/Figure5_C.png", map_plot_Meso, width = 3000, height = 2600, units = "px")

Reading layer `coastline_albers' from data source 
  `C:\E\gitRepos\rwibald\shape_files\coastline_albers.shp' using driver `ESRI Shapefile'
Simple feature collection with 44 features and 4 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -1887741 ymin: -4840771 xmax: 2121462 ymax: -1029671
Projected CRS: GDA94 / Australian Albers
Reading layer `biomes_crisp_3577' from data source 
  `C:\E\gitRepos\rwibald\shape_files\biomes_crisp_3577\biomes_crisp_3577.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 5 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -1887414 ymin: -4840588 xmax: 2121442 ymax: -1087816
Projected CRS: GDA94 / Australian Albers

Generate biome map with paleo-endemics figure 5D

Here we show the specimen data for all of the paleo-endemic taxa categorised by RWiBaLD overlaid on the biome map published by Crisp et al. in 2004.

#loadfonts()

#myFont <- choose_font(c("HelvLight", "Arial", "sans"), quiet = TRUE) #load a font if available

map_text <- "Categorical Analysis of Neo- And Paleo- Endemism"

col_scheme <- c("aseasonal wet" = "cornflowerblue",
                "eremean" = "lightyellow1",
                "monsoonal tropics" = "powderblue",
                "southeastern temperate" = "moccasin",
                "southwestern temperate"= "#fbb4ae")
legend_order <-c("aseasonal wet", 
                 "eremean", 
                 "monsoonal tropics",
                 "southeastern temperate", 
                 "southwestern temperate")
legend_labels <- c("aseasonal wet" = "aseasonal wet",
                   "eremean" = "eremean",
                   "monsoonal tropics" = "monsoonal tropics",
                   "southeastern temperate"="southeastern temperate",
                   "southwestern temperate"="southwestern temperate")

map_shape_file1 <- paste0("shape_files/coastline_albers.shp")
map_data  <- st_read(map_shape_file1)   
#geom_sf(data = map_data, colour = "grey77" , fill="transparent") +

map_shape_file <- paste0("shape_files/biomes_crisp_3577/biomes_crisp_3577.shp")

# Read the shapefile as an sf object
biomes_sf <- st_read(map_shape_file)

csv_file <- paste0("quarto_outputs/Paleo_endemics.csv") 

# Read the CSV file into a data frame
csv_data <- read.csv(csv_file)

# Convert data frame to sf object
csv_sf <- st_as_sf(csv_data, coords = c("x_metres_EPSG_3577_Albers_Equal_Area", "y_metres_EPSG_3577_Albers_Equal_Area"), crs = st_crs(biomes_sf))

# Plot the sf object with ggplot2, coloring by the BIOME column
map_plot_Paleo  <- ggplot() +
  geom_sf(data = biomes_sf, aes(fill = BIOME), color = "grey77") +
  geom_sf(data = map_data, colour = "grey77" , fill="transparent") +
  geom_sf(data = csv_sf, fill= "royalblue3", color = "darkblue", shape = 21) +
  scale_fill_manual(values = col_scheme,  labels=legend_labels, name="Biomes", guide = guide_legend(direction = "horizontal", title.position = "bottom", title.hjust=0.5, title.vjust=0.5, label.position="bottom", label.hjust = 0.5, label.vjust = 0.1, lineheight=0.5))+
    theme(text = element_text(family = myFont),
         strip.background = element_blank(),
         title = element_text(colour = 'black', angle = 0, size=rel(1), face = 'plain', family = myFont),
         axis.line=element_blank(),axis.text.x=element_blank(),
         axis.text.y=element_blank(),axis.ticks=element_blank(),
         axis.title.x=element_blank(), axis.title.y=element_blank(),
         legend.position="none",
         legend.direction='horizontal',
         legend.text = element_text(colour = 'black', angle = 0, size=rel(0.5), face = 'plain', family = myFont),
         legend.key = element_rect(color = "black", size = 0),
         panel.grid = element_blank(),
         panel.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
         panel.border = element_blank(),
         plot.background=element_blank(),#element_rect(colour = "black", fill="white", size = 1),
         plot.margin=unit(c(0,0,0,0),"line"))
  

print(map_plot_Paleo)

ggsave("quarto_outputs/figures/Figure5_D.png", map_plot_Paleo, width = 3000, height = 2600, units = "px")

Reading layer `coastline_albers' from data source 
  `C:\E\gitRepos\rwibald\shape_files\coastline_albers.shp' using driver `ESRI Shapefile'
Simple feature collection with 44 features and 4 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -1887741 ymin: -4840771 xmax: 2121462 ymax: -1029671
Projected CRS: GDA94 / Australian Albers
Reading layer `biomes_crisp_3577' from data source 
  `C:\E\gitRepos\rwibald\shape_files\biomes_crisp_3577\biomes_crisp_3577.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 5 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -1887414 ymin: -4840588 xmax: 2121442 ymax: -1087816
Projected CRS: GDA94 / Australian Albers

Generate 4 up map figure 5ABCD

Here we compile the maps into one figure 5A-D

patchwork_map <- map_plot_CANAPE +
              map_plot_Neo + 
              map_plot_Meso + 
              map_plot_Paleo

patchwork_map <- patchwork_map +
              plot_annotation(subtitle = '',
                              caption = '',
                              tag_levels = 'A') +
              plot_layout(ncol = 2, guides = 'collect') &
              theme(legend.position = "bottom", 
                    legend.title=element_text(size=rel(0.6)),
                    legend.text=element_text(size=rel(0.5)),
                    legend.title.position = "bottom",
                    legend.label.position= "bottom",
                    legend.key = element_rect(color = "black", size = 0.5),
                    legend.key.height = unit(0.5, "cm"),
                    legend.key.width = unit(0.5, "cm"),
                    plot.tag.position = c(0.9, 0.9),
                    plot.tag = element_text(size = 12, hjust = 0, vjust = 0))
             
#print(patchwork_map)

ggsave('quarto_outputs/figures/Figure5_ABCD.png', patchwork_map, width = 3000, height = 2600, units = "px")

Table of RWiBaLD results supplementary table 1

RWiBaLD results for each branch on the phylogeny of Australian Acacia.

RWiBaLD_results_csv  <- paste0("quarto_outputs/Acacia_RWiBaLD_results_all_with_range.csv")

rwibald_results_all_with_range <- read.table(RWiBaLD_results_csv, header=T,sep=",")

#View(rwibald_results_all_with_range)

#colnames(rwibald_results_all_with_range)

gt_table <- rwibald_results_all_with_range %>%
            select("NAME", "branch_length_comparison_tree", "branch_length_observed_tree",
                   "rwibald_score", "rwibald_type", "range_cell_count")

# print table using gt
rwibald_results_all_with_range_table <- gt(gt_table) %>%
  tab_options(
    table.width = pct(100),
    table.layout = "auto",
    table.align = "left",
    table.margin.left = px(5),
    table.margin.right = px(5),
    table.font.size = px(8),
    column_labels.font.size = px(10),
    heading.align = "center",
    heading.title.font.size = px(12),
    quarto.use_bootstrap = TRUE
  ) %>%
  opt_row_striping() %>%
  tab_header(
    title = md("RWiBaLD Results")
  ) %>%
  tab_style(
    style = cell_text(align = "center"),
    locations = cells_body()
  )

rwibald_results_all_with_range_table

RWiBaLD Results
NAME	branch_length_comparison_tree	branch_length_observed_tree	rwibald_score	rwibald_type	range_cell_count
1___	8.777963e-05	2.234812e-04	1.357015e-04	other	103
10___	8.951784e-05	3.583689e-05	-5.368095e-05	other	101
100___	4.109683e-05	7.239508e-06	-3.385732e-05	other	220
101___	2.366833e-05	3.361011e-05	9.941780e-06	other	382
102___	2.318283e-05	1.884262e-05	-4.340207e-06	other	390
103___	2.137424e-05	4.697007e-05	2.559583e-05	other	423
104___	2.318283e-04	9.137027e-04	6.818744e-04	other	39
105___	6.027535e-04	1.624921e-03	1.022168e-03	other	15
106___	1.532424e-04	1.057493e-04	-4.749313e-05	other	59
107___	9.935497e-05	3.545809e-04	2.552259e-04	other	91
108___	9.827502e-05	3.429999e-05	-6.397503e-05	other	92
109___	8.449815e-05	1.130098e-04	2.851166e-05	other	107
11___	8.777963e-05	2.896118e-05	-5.881846e-05	other	103
110___	1.891486e-05	9.029480e-06	-9.885378e-06	other	478
111___	7.534419e-04	1.343083e-03	5.896411e-04	other	12
112___	1.614518e-04	4.342113e-05	-1.180307e-04	other	56
113___	1.051314e-04	1.208033e-04	1.567189e-05	other	86
114___	8.777963e-05	6.943275e-09	-8.777269e-05	other	103
115___	7.862002e-05	9.885975e-06	-6.873404e-05	other	115
116___	7.175637e-05	1.346953e-05	-5.828684e-05	other	126
117___	7.008761e-05	4.083340e-05	-2.925421e-05	other	129
118___	7.008761e-05	5.616836e-05	-1.391925e-05	other	129
119___	6.954848e-05	1.778353e-05	-5.176495e-05	other	130
12___	1.335495e-05	1.097784e-05	-2.377112e-06	other	677
120___	1.597403e-05	7.837726e-06	-8.136307e-06	other	566
121___	1.504376e-05	1.467145e-05	-3.723155e-07	other	601
122___	9.881205e-06	4.100078e-06	-5.781126e-06	other	915
123___	9.859654e-06	1.414163e-06	-8.445490e-06	other	917
124___	3.117690e-04	3.328489e-04	2.107982e-05	other	29
125___	2.511473e-04	3.887985e-04	1.376512e-04	other	36
126___	2.054841e-04	4.573766e-05	-1.597465e-04	other	44
127___	2.825407e-04	6.909660e-05	-2.134441e-04	other	32
128___	2.825407e-04	2.411957e-04	-4.134496e-05	other	32
129___	5.022946e-04	2.914361e-04	-2.108585e-04	other	18
13___	1.310334e-05	5.646846e-06	-7.456490e-06	other	690
130___	1.130163e-04	3.901460e-05	-7.400168e-05	other	80
131___	1.004589e-04	4.157142e-05	-5.888750e-05	other	90
132___	7.862002e-05	1.278658e-04	4.924578e-05	other	115
133___	7.662121e-05	3.954025e-05	-3.708095e-05	other	118
134___	3.863804e-05	1.794236e-06	-3.684381e-05	other	234
135___	4.346780e-05	4.264787e-05	-8.199310e-07	other	208
136___	6.551668e-05	4.443585e-05	-2.108084e-05	other	138
137___	2.807858e-05	5.238707e-05	2.430849e-05	other	322
138___	2.539692e-05	7.418464e-05	4.878772e-05	other	356
139___	4.758580e-04	6.521900e-05	-4.106390e-04	other	19
14___	1.145919e-05	4.368943e-06	-7.090249e-06	other	789
140___	3.767209e-04	2.539890e-04	-1.227320e-04	other	24
141___	3.477424e-04	5.308590e-04	1.831166e-04	other	26
142___	2.916549e-04	4.352484e-04	1.435935e-04	other	31
143___	2.054841e-04	2.656276e-05	-1.789214e-04	other	44
144___	1.506884e-04	1.191929e-08	-1.506765e-04	other	60
145___	1.238535e-04	5.947281e-05	-6.438065e-05	other	73
146___	1.221798e-04	3.874051e-05	-8.343925e-05	other	74
147___	8.145317e-05	9.120355e-05	9.750379e-06	other	111
148___	1.936039e-05	8.736721e-06	-1.062367e-05	other	467
149___	1.361642e-05	7.233721e-06	-6.382699e-06	other	664
15___	1.090628e-05	2.498268e-05	1.407641e-05	other	829
150___	1.302781e-05	3.732148e-06	-9.295665e-06	other	694
151___	3.931001e-04	1.185664e-04	-2.745337e-04	other	23
152___	2.318283e-04	6.839497e-05	-1.634333e-04	other	39
153___	1.586193e-04	1.557460e-04	-2.873300e-06	other	57
154___	7.008761e-05	6.309607e-06	-6.377801e-05	other	129
155___	6.551668e-05	1.174379e-05	-5.377289e-05	other	138
156___	6.504534e-05	4.422731e-05	-2.081803e-05	other	139
157___	3.631045e-05	1.614817e-05	-2.016228e-05	other	249
158___	5.758791e-05	5.237361e-05	-5.214300e-06	other	157
159___	4.589494e-05	1.856155e-05	-2.733338e-05	other	197
16___	6.901757e-05	9.388402e-05	2.486645e-05	other	131
160___	4.000576e-05	2.116269e-05	-1.884307e-05	other	226
161___	2.620667e-05	2.072920e-09	-2.620460e-05	other	345
162___	1.369894e-04	5.770269e-05	-7.928674e-05	other	66
163___	1.027421e-04	1.694995e-05	-8.579212e-05	other	88
164___	8.001152e-05	3.946034e-05	-4.055118e-05	other	113
165___	5.318413e-05	4.206808e-09	-5.317992e-05	other	170
166___	4.224908e-05	3.435192e-06	-3.881388e-05	other	214
167___	9.132629e-05	2.496945e-05	-6.635684e-05	other	99
168___	2.825407e-04	4.848574e-05	-2.340550e-04	other	32
169___	1.674315e-04	2.781283e-05	-1.396187e-04	other	54
17___	6.192673e-05	8.906534e-05	2.713862e-05	other	146
170___	1.205507e-04	3.798806e-05	-8.256264e-05	other	75
171___	1.205507e-04	5.692448e-05	-6.362621e-05	other	75
172___	1.205507e-04	1.042297e-04	-1.632098e-05	other	75
173___	1.174195e-04	4.595375e-05	-7.146576e-05	other	77
174___	1.063683e-04	1.635680e-06	-1.047326e-04	other	85
175___	8.777963e-05	6.665401e-06	-8.111423e-05	other	103
176___	5.079383e-05	4.017738e-09	-5.078982e-05	other	178
177___	4.967749e-05	1.645418e-05	-3.322331e-05	other	182
178___	3.631045e-05	2.872118e-09	-3.630758e-05	other	249
179___	2.816605e-05	1.232424e-05	-1.584181e-05	other	321
18___	2.379290e-04	2.770606e-05	-2.102229e-04	other	38
180___	2.226922e-05	2.067711e-06	-2.020151e-05	other	406
181___	1.583415e-05	9.804553e-06	-6.029602e-06	other	571
182___	1.382462e-05	2.249130e-06	-1.157549e-05	other	654
183___	5.795707e-05	6.485613e-05	6.899068e-06	other	156
184___	5.413953e-05	3.121126e-05	-2.292827e-05	other	167
185___	8.371576e-05	6.061842e-05	-2.309734e-05	other	108
186___	8.072591e-05	6.385333e-09	-8.071953e-05	other	112
187___	7.930967e-05	2.326218e-05	-5.604749e-05	other	114
188___	7.862002e-05	3.879562e-05	-3.982440e-05	other	115
189___	6.027535e-04	6.557666e-04	5.301308e-05	other	15
19___	9.827502e-05	6.119933e-05	-3.707570e-05	other	92
190___	2.659207e-04	1.346622e-04	-1.312585e-04	other	34
191___	2.443595e-04	1.932858e-08	-2.443402e-04	other	37
192___	7.727609e-05	2.609021e-05	-5.118588e-05	other	117
193___	6.747241e-05	4.129034e-05	-2.618207e-05	other	134
194___	5.318413e-04	7.492230e-05	-4.569190e-04	other	17
195___	1.923681e-04	1.878672e-04	-4.500934e-06	other	47
196___	6.901757e-05	4.168372e-05	-2.733386e-05	other	131
197___	6.551668e-05	6.230231e-06	-5.928645e-05	other	138
198___	6.551668e-05	2.481117e-05	-4.070552e-05	other	138
199___	6.192673e-05	3.577419e-05	-2.615254e-05	other	146
2___	8.529530e-05	1.408452e-04	5.554991e-05	other	106
20___	4.367779e-05	4.996096e-05	6.283174e-06	other	207
200___	5.686354e-05	4.728088e-05	-9.582659e-06	other	159
201___	3.847363e-05	1.174597e-05	-2.672766e-05	other	235
202___	3.631045e-05	4.837139e-06	-3.147331e-05	other	249
203___	3.490850e-05	3.357560e-06	-3.155094e-05	other	259
204___	4.520651e-04	8.089279e-05	-3.711723e-04	other	20
205___	2.009178e-04	4.073891e-05	-1.601789e-04	other	45
206___	1.586193e-04	2.724207e-05	-1.313773e-04	other	57
207___	4.684613e-05	3.858457e-06	-4.298767e-05	other	193
208___	4.636565e-05	1.780143e-06	-4.458551e-05	other	195
209___	4.018357e-05	3.683896e-06	-3.649967e-05	other	225
21___	4.367779e-05	5.050867e-05	6.830880e-06	other	207
210___	3.490850e-05	3.157915e-06	-3.175059e-05	other	259
211___	4.758580e-04	3.478897e-04	-1.279683e-04	other	19
212___	1.004589e-03	1.252213e-04	-8.793679e-04	neo-endemic	9
213___	3.767209e-04	2.979822e-08	-3.766911e-04	other	24
214___	2.916549e-04	1.445634e-04	-1.470915e-04	other	31
215___	2.825407e-04	2.282277e-05	-2.597179e-04	other	32
216___	2.205196e-04	1.832734e-05	-2.021922e-04	other	41
217___	1.772804e-04	1.402269e-08	-1.772664e-04	other	51
218___	1.643873e-04	1.300286e-08	-1.643743e-04	other	55
219___	1.558845e-04	1.233030e-08	-1.558722e-04	other	58
22___	3.183557e-05	2.970162e-05	-2.133952e-06	other	284
220___	1.255736e-04	9.932740e-09	-1.255637e-04	other	72
221___	3.437758e-05	9.878504e-06	-2.449907e-05	other	263
222___	6.901757e-05	5.459216e-09	-6.901212e-05	other	131
223___	6.412271e-05	1.364680e-05	-5.047591e-05	other	141
224___	2.102628e-04	1.663157e-08	-2.102462e-04	other	43
225___	3.477424e-04	5.693195e-05	-2.908104e-04	other	26
226___	2.511473e-04	4.780370e-05	-2.033436e-04	other	36
227___	2.511473e-04	1.986548e-08	-2.511274e-04	other	36
228___	2.443595e-04	1.932858e-08	-2.443402e-04	other	37
229___	2.443595e-04	5.971620e-05	-1.846433e-04	other	37
23___	3.064848e-05	1.226722e-04	9.202372e-05	other	295
230___	5.349883e-05	4.231700e-09	-5.349460e-05	other	169
231___	5.051007e-05	1.239962e-05	-3.811045e-05	other	179
232___	4.834921e-05	3.824371e-09	-4.834539e-05	other	187
233___	1.063683e-04	8.413615e-09	-1.063598e-04	other	85
234___	1.369894e-04	3.542795e-05	-1.015615e-04	other	66
235___	1.329603e-04	4.690909e-05	-8.605124e-05	other	68
236___	1.883605e-04	1.489911e-08	-1.883456e-04	other	48
237___	2.916549e-04	7.235236e-05	-2.193026e-04	other	31
238___	4.109683e-04	4.873219e-05	-3.622361e-04	other	22
239___	1.808260e-04	1.638970e-05	-1.644363e-04	other	50
24___	2.147578e-05	9.431207e-06	-1.204457e-05	other	421
240___	9.935497e-05	8.749122e-06	-9.060585e-05	other	91
241___	9.721830e-05	7.689864e-09	-9.721061e-05	other	93
242___	5.650814e-04	1.624080e-04	-4.026734e-04	other	16
243___	4.809203e-05	1.864923e-05	-2.944280e-05	other	188
244___	4.367779e-05	7.142586e-06	-3.653520e-05	other	207
245___	8.610764e-05	6.811022e-09	-8.610083e-05	other	105
246___	8.001152e-05	6.669451e-06	-7.334207e-05	other	113
247___	5.226186e-05	4.377641e-06	-4.788422e-05	other	173
248___	2.790525e-05	2.207276e-09	-2.790305e-05	other	324
249___	2.739789e-05	2.475063e-06	-2.492282e-05	other	330
25___	1.891486e-05	4.604526e-06	-1.431033e-05	other	478
250___	2.628286e-05	2.078946e-09	-2.628078e-05	other	344
251___	2.490717e-05	2.349888e-06	-2.255728e-05	other	363
252___	2.477069e-05	1.959335e-09	-2.476873e-05	other	365
253___	2.404602e-05	2.432616e-06	-2.161340e-05	other	376
254___	2.199830e-05	4.456066e-06	-1.754224e-05	other	411
255___	2.189177e-05	2.214796e-06	-1.967698e-05	other	413
256___	2.142489e-05	1.694686e-09	-2.142319e-05	other	422
257___	1.982742e-05	6.024675e-06	-1.380274e-05	other	456
258___	1.948557e-05	1.541287e-09	-1.948402e-05	other	464
259___	1.833936e-05	3.784195e-06	-1.455516e-05	other	493
26___	1.848937e-05	4.457219e-06	-1.403215e-05	other	489
260___	1.783294e-05	1.391071e-06	-1.644187e-05	other	507
261___	1.779784e-05	5.150657e-06	-1.264718e-05	other	508
262___	1.620305e-05	2.735927e-06	-1.346712e-05	other	558
263___	1.569671e-05	2.878626e-06	-1.281808e-05	other	576
264___	9.398443e-06	8.694522e-06	-7.039213e-07	other	962
265___	8.907687e-06	9.213716e-07	-7.986315e-06	other	1015
266___	8.898920e-06	2.122882e-05	1.232990e-05	other	1016
267___	1.089314e-04	1.554625e-04	4.653110e-05	other	83
268___	1.808260e-03	1.430315e-07	-1.808117e-03	neo-endemic	5
269___	4.109683e-04	2.854363e-05	-3.824247e-04	other	22
27___	1.231785e-05	1.413074e-05	1.812889e-06	other	734
270___	4.612909e-05	7.841023e-06	-3.828807e-05	other	196
271___	4.018357e-05	4.209988e-06	-3.597358e-05	other	225
272___	4.000576e-05	3.164413e-09	-4.000260e-05	other	226
273___	3.913984e-05	1.314225e-05	-2.599759e-05	other	231
274___	3.880387e-05	4.930536e-06	-3.387334e-05	other	233
275___	3.847363e-05	1.730259e-05	-2.117104e-05	other	235
276___	3.736075e-05	6.705231e-06	-3.065552e-05	other	242
277___	2.897853e-05	7.521549e-06	-2.145698e-05	other	312
278___	2.102628e-04	1.287104e-04	-8.155248e-05	other	43
279___	8.371576e-05	6.621827e-09	-8.370914e-05	other	108
28___	1.110725e-05	2.269639e-05	1.158914e-05	other	814
280___	1.506884e-03	6.490422e-04	-8.578415e-04	neo-endemic	6
281___	6.458073e-04	2.256771e-03	1.610963e-03	other	14
282___	5.349883e-05	1.031773e-05	-4.318110e-05	other	169
283___	3.705452e-05	6.575738e-06	-3.047878e-05	other	244
284___	5.256571e-05	5.367677e-05	1.111060e-06	other	172
285___	1.412703e-04	8.163462e-06	-1.331069e-04	other	64
286___	4.388982e-05	2.595055e-05	-1.793927e-05	other	206
287___	1.339452e-05	1.474387e-05	1.349347e-06	other	675
288___	5.022946e-04	2.487536e-04	-2.535410e-04	other	18
289___	4.264765e-05	2.549108e-05	-1.715657e-05	other	212
29___	6.551668e-05	6.175468e-05	-3.762008e-06	other	138
290___	1.197523e-05	3.172861e-06	-8.802374e-06	other	755
291___	1.160629e-05	7.321023e-06	-4.285270e-06	other	779
292___	7.410904e-05	5.032021e-05	-2.378883e-05	other	122
293___	1.390970e-04	1.205114e-05	-1.270458e-04	other	65
294___	6.067988e-05	4.193568e-05	-1.874420e-05	other	149
295___	5.948225e-05	4.704982e-09	-5.947755e-05	other	152
296___	5.615716e-05	4.441971e-09	-5.615272e-05	other	161
297___	5.446568e-05	2.553282e-05	-2.893286e-05	other	166
298___	4.453843e-05	4.152567e-05	-3.012765e-06	other	203
299___	2.605563e-05	1.171474e-05	-1.434089e-05	other	347
3___	8.449815e-05	2.284378e-05	-6.165437e-05	other	107
30___	7.175637e-05	2.194683e-04	1.477120e-04	other	126
300___	5.137104e-05	6.357156e-05	1.220052e-05	other	176
301___	3.264008e-05	2.424527e-05	-8.394812e-06	other	277
302___	1.205507e-04	9.535431e-09	-1.205412e-04	other	75
303___	5.758791e-05	1.258129e-05	-4.500663e-05	other	157
304___	4.660465e-05	2.054523e-05	-2.605942e-05	other	194
305___	4.913751e-05	1.646566e-05	-3.267185e-05	other	184
306___	3.117690e-04	3.921233e-05	-2.725567e-04	other	29
307___	3.229037e-04	1.402147e-04	-1.826889e-04	other	28
308___	1.705906e-04	9.908724e-05	-7.150337e-05	other	53
309___	1.116210e-04	1.032886e-05	-1.012922e-04	other	81
31___	3.616521e-05	6.754410e-05	3.137889e-05	other	250
310___	7.350652e-05	3.040135e-05	-4.310517e-05	other	123
311___	3.559568e-05	5.896858e-06	-2.969882e-05	other	254
312___	3.054494e-05	2.224148e-05	-8.303462e-06	other	296
313___	2.907171e-05	7.115461e-06	-2.195625e-05	other	311
314___	2.288937e-05	3.143992e-05	8.550544e-06	other	395
315___	1.558845e-05	1.982681e-06	-1.360577e-05	other	580
316___	1.448927e-05	6.333613e-06	-8.155654e-06	other	624
317___	1.134417e-05	7.667768e-06	-3.676400e-06	other	797
318___	1.120360e-05	8.344771e-06	-2.858826e-06	other	807
319___	1.113461e-05	7.691499e-07	-1.036546e-05	other	812
32___	2.659207e-04	3.391338e-04	7.321315e-05	other	34
320___	9.320930e-06	1.738357e-06	-7.582573e-06	other	970
321___	4.758580e-04	4.871288e-04	1.127079e-05	other	19
322___	4.109683e-04	2.386360e-04	-1.723323e-04	other	22
323___	2.443595e-04	3.106113e-05	-2.132984e-04	other	37
324___	1.291615e-04	1.215317e-04	-7.629775e-06	other	70
325___	2.731511e-05	1.991416e-05	-7.400957e-06	other	331
326___	2.723284e-05	3.042749e-05	3.194652e-06	other	332
327___	2.605563e-05	7.121782e-06	-1.893384e-05	other	347
328___	2.561275e-05	9.605731e-06	-1.600702e-05	other	353
329___	8.449815e-05	2.003453e-05	-6.446362e-05	other	107
33___	8.371576e-05	1.184969e-04	3.478110e-05	other	108
330___	8.219366e-05	4.823878e-05	-3.395488e-05	other	110
331___	7.794226e-05	7.278825e-06	-7.066344e-05	other	116
332___	6.322589e-05	5.296122e-06	-5.792977e-05	other	143
333___	1.808260e-04	2.032569e-05	-1.605004e-04	other	50
334___	4.589494e-05	3.630240e-09	-4.589131e-05	other	197
335___	3.013767e-04	4.297014e-05	-2.584066e-04	other	30
336___	2.825407e-04	2.263536e-05	-2.599053e-04	other	32
337___	5.581051e-05	1.341677e-05	-4.239374e-05	other	162
338___	5.137104e-05	4.136785e-06	-4.723425e-05	other	176
339___	3.299745e-05	8.427732e-06	-2.456972e-05	other	274
34___	6.412271e-05	8.952333e-05	2.540062e-05	other	141
340___	2.667051e-05	1.318683e-05	-1.348368e-05	other	339
341___	2.477069e-05	6.546084e-06	-1.822461e-05	other	365
342___	1.868038e-05	3.678263e-06	-1.500211e-05	other	484
343___	1.801056e-05	3.051310e-06	-1.495925e-05	other	502
344___	1.786819e-05	8.811448e-06	-9.056739e-06	other	506
345___	7.233042e-06	3.328682e-06	-3.904360e-06	other	1250
346___	7.102358e-06	5.151363e-06	-1.950995e-06	other	1273
347___	7.096784e-06	1.117101e-06	-5.979683e-06	other	1274
348___	6.023519e-06	9.637247e-07	-5.059794e-06	other	1501
349___	5.851976e-06	2.989433e-06	-2.862542e-06	other	1545
35___	2.456876e-05	4.002870e-05	1.545994e-05	other	368
350___	5.755126e-06	1.657232e-06	-4.097893e-06	other	1571
351___	5.693515e-06	2.432663e-06	-3.260852e-06	other	1588
352___	1.369894e-04	3.417201e-04	2.047307e-04	other	66
353___	4.758580e-05	1.997795e-04	1.521937e-04	other	190
354___	3.477424e-04	8.157094e-05	-2.661715e-04	other	26
355___	2.583229e-04	2.894556e-04	3.113269e-05	other	35
356___	4.018357e-05	1.878571e-05	-2.139786e-05	other	225
357___	3.477424e-05	8.403271e-06	-2.637097e-05	other	260
358___	2.964361e-05	1.034821e-04	7.383850e-05	other	305
359___	1.144469e-04	9.052624e-09	-1.144378e-04	other	79
36___	1.808260e-03	8.962423e-04	-9.120182e-04	neo-endemic	5
360___	3.023847e-05	2.099901e-05	-9.239463e-06	other	299
361___	2.456876e-05	8.875664e-06	-1.569309e-05	other	368
362___	2.443595e-05	1.245297e-05	-1.198298e-05	other	370
363___	2.312354e-05	2.369180e-05	5.682603e-07	other	391
364___	2.277406e-05	7.003495e-06	-1.577057e-05	other	397
365___	1.435127e-04	7.938922e-05	-6.412351e-05	other	63
366___	1.189645e-04	2.148619e-05	-9.747832e-05	other	76
367___	2.054841e-04	1.625358e-08	-2.054679e-04	other	44
368___	7.534419e-05	4.042876e-05	-3.491543e-05	other	120
369___	6.797972e-05	6.320342e-06	-6.165937e-05	other	133
37___	1.130163e-03	1.957058e-03	8.268952e-04	meso-endemic	8
370___	4.520651e-04	3.575787e-08	-4.520294e-04	other	20
371___	9.041302e-04	2.486922e-04	-6.554380e-04	other	10
372___	1.965500e-04	4.626448e-05	-1.502856e-04	other	46
373___	1.369894e-04	5.996121e-05	-7.702822e-05	other	66
374___	1.004589e-04	6.885180e-06	-9.357373e-05	other	90
375___	1.102598e-04	1.954259e-05	-9.071720e-05	other	82
376___	1.089314e-04	3.756057e-05	-7.137078e-05	other	83
377___	9.418023e-05	2.944702e-05	-6.473321e-05	other	96
378___	5.870976e-05	7.586922e-05	1.715946e-05	other	154
379___	3.675326e-05	2.170961e-05	-1.504365e-05	other	246
38___	1.956992e-05	1.085814e-05	-8.711777e-06	other	462
380___	3.161295e-05	1.208417e-05	-1.952877e-05	other	286
381___	3.054494e-05	8.321486e-06	-2.222345e-05	other	296
382___	2.925988e-05	1.085457e-05	-1.840531e-05	other	309
383___	2.226922e-05	8.843135e-06	-1.342608e-05	other	406
384___	1.801056e-05	1.732309e-06	-1.627825e-05	other	502
385___	7.534419e-05	4.831432e-05	-2.702987e-05	other	120
386___	5.166458e-05	3.578376e-05	-1.588083e-05	other	175
387___	3.183557e-05	7.953268e-06	-2.388230e-05	other	284
388___	2.674942e-05	4.289230e-05	1.614289e-05	other	338
389___	5.650814e-04	2.012442e-04	-3.638372e-04	other	16
39___	7.794226e-05	1.943760e-05	-5.850466e-05	other	116
390___	4.305382e-05	5.845142e-05	1.539760e-05	other	210
391___	4.244743e-05	2.840161e-05	-1.404582e-05	other	213
392___	4.224908e-05	4.088467e-06	-3.816061e-05	other	214
393___	4.072659e-05	4.482355e-06	-3.624423e-05	other	222
394___	2.723284e-05	3.486406e-06	-2.374643e-05	other	332
395___	2.031753e-05	2.016143e-06	-1.830139e-05	other	445
396___	1.504376e-05	1.994775e-06	-1.304899e-05	other	601
397___	1.221798e-05	3.855233e-06	-8.362743e-06	other	740
398___	9.447547e-06	2.013047e-06	-7.434500e-06	other	957
399___	8.744006e-06	4.227835e-06	-4.516171e-06	other	1034
4___	8.145317e-05	7.823254e-05	-3.220634e-06	other	111
40___	1.159141e-04	9.524741e-05	-2.066672e-05	other	78
400___	8.529530e-06	9.422414e-07	-7.587289e-06	other	1060
401___	1.291615e-03	1.535655e-04	-1.138049e-03	neo-endemic	7
402___	6.027535e-04	6.599641e-04	5.721066e-05	other	15
403___	1.130163e-04	2.535538e-05	-8.766090e-05	other	80
404___	2.659207e-04	1.328233e-04	-1.330974e-04	other	34
405___	1.015877e-04	1.894606e-05	-8.264160e-05	other	89
406___	1.860350e-05	1.625219e-06	-1.697828e-05	other	486
407___	1.797476e-05	1.502893e-05	-2.945824e-06	other	503
408___	1.086695e-05	4.145344e-06	-6.721606e-06	other	832
409___	4.264765e-05	1.735445e-05	-2.529321e-05	other	212
41___	1.323763e-05	1.047082e-09	-1.323658e-05	other	683
410___	3.490850e-05	3.545571e-06	-3.136293e-05	other	259
411___	3.172387e-05	3.150578e-06	-2.857329e-05	other	285
412___	3.054494e-05	5.381137e-06	-2.516380e-05	other	296
413___	1.030935e-05	5.404304e-06	-4.905049e-06	other	877
414___	9.700968e-06	5.151312e-06	-4.549656e-06	other	932
415___	9.359526e-06	3.100889e-06	-6.258637e-06	other	966
416___	8.219366e-06	9.510136e-07	-7.268352e-06	other	1100
417___	4.758580e-05	1.809485e-05	-2.949095e-05	other	190
418___	4.498160e-05	7.975766e-06	-3.700584e-05	other	201
419___	4.305382e-05	4.236547e-05	-6.883487e-07	other	210
42___	1.808260e-04	1.509412e-05	-1.657319e-04	other	50
420___	3.139341e-05	4.115266e-06	-2.727814e-05	other	288
421___	2.360653e-05	1.374304e-05	-9.863493e-06	other	383
422___	5.833098e-05	6.995193e-06	-5.133579e-05	other	155
423___	3.948167e-05	9.585672e-06	-2.989600e-05	other	229
424___	1.699493e-05	3.944849e-06	-1.305008e-05	other	532
425___	1.403929e-05	4.802965e-06	-9.236324e-06	other	644
426___	1.004589e-03	1.669130e-03	6.645407e-04	meso-endemic	9
427___	6.954848e-04	1.737355e-05	-6.781112e-04	other	13
428___	7.410904e-05	1.886828e-04	1.145737e-04	other	122
429___	3.117690e-04	2.182921e-04	-9.347694e-05	other	29
43___	1.280638e-05	1.054689e-06	-1.175169e-05	other	706
430___	1.586193e-04	1.105759e-04	-4.804341e-05	other	57
431___	5.318413e-04	1.050486e-04	-4.267927e-04	other	17
432___	1.102598e-04	3.772722e-05	-7.253257e-05	other	82
433___	6.849471e-05	3.473418e-05	-3.376054e-05	other	132
434___	4.072659e-05	4.604166e-06	-3.612242e-05	other	222
435___	2.059522e-05	1.004895e-05	-1.054628e-05	other	439
436___	8.978453e-06	4.442727e-06	-4.535726e-06	other	1007
437___	8.545654e-06	9.886114e-07	-7.557043e-06	other	1058
438___	5.587949e-06	4.420008e-10	-5.587507e-06	other	1618
439___	5.587949e-06	4.381861e-07	-5.149763e-06	other	1618
44___	1.072515e-05	3.076302e-06	-7.648849e-06	other	843
440___	5.512989e-06	3.195335e-06	-2.317654e-06	other	1640
441___	6.551668e-05	6.641427e-05	8.975846e-07	other	138
442___	7.008761e-05	5.543855e-09	-7.008207e-05	other	129
443___	9.320930e-05	7.372756e-09	-9.320193e-05	other	97
444___	8.777963e-05	3.802415e-05	-4.975549e-05	other	103
445___	4.410391e-05	1.069986e-05	-3.340405e-05	other	205
446___	3.675326e-05	4.103422e-05	4.280956e-06	other	246
447___	2.756495e-05	8.186036e-06	-1.937891e-05	other	328
448___	5.196151e-05	4.063185e-05	-1.132966e-05	other	174
449___	4.072659e-05	8.142923e-06	-3.258366e-05	other	222
45___	1.015877e-05	4.644428e-06	-5.514339e-06	other	890
450___	2.916549e-05	8.171119e-06	-2.099437e-05	other	310
451___	2.674942e-05	6.829576e-06	-1.991984e-05	other	338
452___	9.618407e-05	3.831646e-05	-5.786760e-05	other	94
453___	6.235381e-05	1.962881e-05	-4.272500e-05	other	145
454___	5.909348e-05	1.595873e-05	-4.313475e-05	other	153
455___	2.102628e-04	2.624281e-04	5.216525e-05	other	43
456___	6.648016e-05	1.862801e-05	-4.785215e-05	other	136
457___	3.531759e-05	6.913559e-06	-2.840403e-05	other	256
458___	2.964361e-05	1.474459e-06	-2.816915e-05	other	305
459___	2.879396e-05	4.898673e-06	-2.389528e-05	other	314
46___	9.859654e-06	8.616349e-07	-8.998019e-06	other	917
460___	1.725439e-05	6.624631e-06	-1.062976e-05	other	524
461___	1.225109e-05	2.715107e-06	-9.535980e-06	other	738
462___	3.602112e-05	2.849232e-09	-3.601828e-05	other	251
463___	2.398223e-05	1.110051e-05	-1.288172e-05	other	377
464___	2.348390e-05	1.811581e-05	-5.368096e-06	other	385
465___	2.330233e-05	1.843189e-09	-2.330048e-05	other	388
466___	2.064224e-05	1.996282e-06	-1.864596e-05	other	438
467___	4.367779e-05	3.392275e-05	-9.755037e-06	other	207
468___	3.798867e-05	2.833669e-06	-3.515500e-05	other	238
469___	2.483874e-05	1.720410e-05	-7.634641e-06	other	364
47___	7.591354e-06	1.079876e-05	3.207411e-06	other	1191
470___	2.511473e-04	1.074039e-04	-1.437433e-04	other	36
471___	6.648016e-05	1.763197e-05	-4.884819e-05	other	136
472___	1.051314e-04	5.013550e-05	-5.499592e-05	other	86
473___	5.196151e-05	7.730290e-06	-4.423122e-05	other	174
474___	9.320930e-05	3.531953e-05	-5.788977e-05	other	97
475___	4.709012e-05	4.027978e-06	-4.306214e-05	other	192
476___	3.782972e-05	3.329100e-06	-3.450062e-05	other	239
477___	2.477069e-05	2.140332e-06	-2.263036e-05	other	365
478___	2.142489e-05	5.868434e-06	-1.555645e-05	other	422
479___	2.117401e-05	1.674841e-09	-2.117234e-05	other	427
48___	7.591354e-06	2.628153e-05	1.869017e-05	other	1191
480___	2.000288e-05	1.869942e-06	-1.813294e-05	other	452
481___	1.165116e-05	1.020156e-06	-1.063101e-05	other	776
482___	9.711388e-06	2.056928e-06	-7.654460e-06	other	931
483___	9.507153e-06	3.151960e-06	-6.355193e-06	other	951
484___	9.014259e-06	4.188186e-06	-4.826074e-06	other	1003
485___	8.987378e-06	2.567102e-06	-6.420276e-06	other	1006
486___	7.681650e-06	3.433618e-06	-4.248032e-06	other	1177
487___	7.572280e-06	2.453788e-06	-5.118492e-06	other	1194
488___	4.002347e-06	9.110017e-07	-3.091346e-06	other	2259
489___	3.995273e-06	2.514326e-06	-1.480946e-06	other	2263
49___	7.584985e-06	2.600874e-06	-4.984111e-06	other	1192
490___	3.782972e-06	3.917105e-07	-3.391261e-06	other	2390
491___	3.778229e-06	1.239022e-06	-2.539207e-06	other	2393
492___	3.778229e-06	3.588184e-07	-3.419411e-06	other	2393
493___	3.771924e-06	4.304886e-07	-3.341436e-06	other	2397
494___	3.771924e-06	8.199410e-07	-2.951983e-06	other	2397
495___	3.708491e-06	1.066736e-06	-2.641756e-06	other	2438
496___	3.679814e-06	1.226428e-06	-2.453386e-06	other	2457
497___	3.661929e-06	6.966459e-07	-2.965283e-06	other	2469
498___	3.130645e-06	3.712458e-06	5.818128e-07	other	2888
499___	3.110183e-06	1.279921e-06	-1.830262e-06	other	2907
5___	2.511473e-04	3.377366e-04	8.658934e-05	other	36
50___	6.987096e-06	4.411483e-06	-2.575613e-06	other	1294
500___	3.088931e-06	7.886079e-07	-2.300324e-06	other	2927
501___	3.058627e-06	2.419341e-10	-3.058385e-06	other	2956
502___	3.057593e-06	4.329675e-06	1.272082e-06	other	2957
503___	3.043185e-06	2.074385e-06	-9.688000e-07	other	2971
504___	3.012763e-06	1.435897e-06	-1.576866e-06	other	3001
505___	2.998774e-06	1.811016e-06	-1.187758e-06	other	3015
506___	2.997779e-06	6.074031e-07	-2.390376e-06	other	3016
507___	0.000000e+00	0.000000e+00	0.000000e+00	other	NA
51___	2.583229e-04	4.403145e-04	1.819916e-04	other	35
52___	2.054841e-04	5.069268e-04	3.014426e-04	other	44
53___	7.534419e-05	3.601855e-05	-3.932564e-05	other	120
54___	1.310334e-04	2.537250e-05	-1.056609e-04	other	69
55___	6.648016e-05	1.839042e-05	-4.808974e-05	other	136
56___	3.948167e-05	1.413389e-05	-2.534778e-05	other	229
57___	3.948167e-05	5.386139e-05	1.437972e-05	other	229
58___	3.913984e-05	5.783681e-05	1.869697e-05	other	231
59___	3.287746e-05	2.911583e-05	-3.761631e-06	other	275
6___	5.795707e-05	1.104020e-04	5.244493e-05	other	156
60___	1.883605e-04	2.228697e-04	3.450926e-05	other	48
61___	1.586193e-04	9.409158e-04	7.822965e-04	other	57
62___	3.931001e-04	2.258312e-04	-1.672689e-04	other	23
63___	3.013767e-04	4.172849e-05	-2.596482e-04	other	30
64___	2.825407e-04	1.212211e-04	-1.613196e-04	other	32
65___	1.845164e-04	8.647190e-05	-9.804447e-05	other	49
66___	1.772804e-04	4.721267e-04	2.948462e-04	other	51
67___	1.532424e-04	2.035616e-04	5.031916e-05	other	59
68___	1.506884e-03	4.977381e-04	-1.009146e-03	neo-endemic	6
69___	2.318283e-04	1.788137e-04	-5.301458e-05	other	39
7___	5.196151e-05	4.778721e-05	-4.174294e-06	other	174
70___	1.051314e-04	4.720912e-05	-5.792230e-05	other	86
71___	1.051314e-04	4.660956e-04	3.609642e-04	other	86
72___	5.022946e-04	1.462622e-03	9.603271e-04	other	18
73___	3.348630e-04	5.881803e-04	2.533172e-04	other	27
74___	6.458073e-04	5.108267e-08	-6.457562e-04	other	14
75___	3.948167e-05	1.461606e-04	1.066789e-04	other	229
76___	3.751578e-05	8.002141e-05	4.250564e-05	other	241
77___	3.690327e-05	9.526445e-06	-2.737683e-05	other	245
78___	3.675326e-05	3.485838e-05	-1.894879e-06	other	246
79___	3.573637e-05	2.826709e-09	-3.573355e-05	other	253
8___	4.566314e-05	1.687415e-05	-2.878899e-05	other	198
80___	3.299745e-05	3.187543e-05	-1.122019e-06	other	274
81___	3.275834e-05	2.286138e-05	-9.896959e-06	other	276
82___	3.217545e-05	2.611769e-05	-6.057764e-06	other	281
83___	3.183557e-05	1.021483e-05	-2.162074e-05	other	284
84___	1.709131e-05	7.851706e-06	-9.239602e-06	other	529
85___	5.693515e-06	7.575179e-06	1.881664e-06	other	1588
86___	8.219366e-04	9.147983e-04	9.286170e-05	other	11
87___	2.216005e-05	1.436167e-05	-7.798385e-06	other	408
88___	2.142489e-05	2.754671e-05	6.121826e-06	other	422
89___	2.107530e-05	4.557120e-05	2.449591e-05	other	429
9___	4.305382e-04	4.710688e-04	4.053056e-05	other	21
90___	6.367114e-05	1.603466e-05	-4.763649e-05	other	142
91___	6.367114e-05	3.151921e-04	2.515210e-04	other	142
92___	5.795707e-05	1.907411e-04	1.327841e-04	other	156
93___	8.072591e-05	8.565735e-06	-7.216018e-05	other	112
94___	7.350652e-05	8.790881e-05	1.440229e-05	other	123
95___	3.240610e-05	5.726527e-05	2.485917e-05	other	279
96___	2.324242e-05	2.470511e-05	1.462687e-06	other	389
97___	1.186523e-05	1.136541e-05	-4.998158e-07	other	762
98___	8.219366e-04	4.798358e-03	3.976421e-03	other	11
99___	4.388982e-05	3.253124e-06	-4.063669e-05	other	206
abbreviata	1.506884e-03	1.436124e-03	-7.075948e-05	meso-endemic	6
acanthaster	9.041302e-04	2.500786e-03	1.596655e-03	other	10
acanthoclada	9.132629e-05	1.844050e-04	9.307874e-05	other	99
acinacea	7.233042e-05	7.218576e-05	-1.446581e-07	other	125
aciphylla	7.534419e-04	3.224805e-04	-4.309613e-04	other	12
acoma	6.458073e-04	3.464592e-04	-2.993482e-04	other	14
acradenia	5.137104e-05	5.621538e-05	4.844342e-06	other	176
acrionastes	1.808260e-03	7.818173e-04	-1.026443e-03	neo-endemic	5
acuaria	2.825407e-04	2.234867e-08	-2.825183e-04	other	32
aculeatissima	2.659207e-04	7.569400e-04	4.910193e-04	other	34
acuminata	6.551668e-05	1.885246e-05	-4.666422e-05	other	138
acutata	3.616521e-04	4.275355e-04	6.588346e-05	other	25
adinophylla	9.041302e-03	1.786382e-02	8.822520e-03	paleo-endemic	1
adnata	1.506884e-03	4.796539e-04	-1.027230e-03	neo-endemic	6
adoxa	7.862002e-05	1.820600e-05	-6.041402e-05	other	115
adsurgens	5.833098e-05	1.054178e-05	-4.778920e-05	other	155
adunca	5.650814e-04	7.893862e-04	2.243048e-04	other	16
aemula	5.022946e-04	3.673275e-04	-1.349671e-04	other	18
aestivalis	3.767209e-04	2.933933e-04	-8.332766e-05	other	24
alata	1.808260e-04	4.009575e-04	2.201315e-04	other	50
alcockii	1.291615e-03	1.606220e-03	3.146051e-04	meso-endemic	7
alexandri	2.260326e-03	9.152274e-03	6.891948e-03	paleo-endemic	4
alpina	5.318413e-04	5.692479e-04	3.740658e-05	other	17
amblygona	1.039230e-04	2.060459e-04	1.021229e-04	other	87
amblyophylla	4.520651e-03	1.408732e-02	9.566664e-03	paleo-endemic	2
ammobia	8.219366e-04	7.910600e-04	-3.087660e-05	other	11
ampliceps	1.116210e-04	5.256877e-05	-5.905225e-05	other	81
amyctica	3.013767e-03	1.040566e-03	-1.973201e-03	neo-endemic	3
anasilla	1.506884e-03	3.428075e-03	1.921192e-03	meso-endemic	6
anastema	1.506884e-03	2.293590e-03	7.867058e-04	meso-endemic	6
anaticeps	6.954848e-04	2.142965e-03	1.447480e-03	other	13
anceps	2.511473e-04	2.015837e-04	-4.956357e-05	other	36
ancistrocarpa	4.346780e-05	7.697054e-05	3.350274e-05	other	208
ancistrophylla	1.238535e-04	2.571802e-05	-9.813544e-05	other	73
andrewsii	2.205196e-04	4.744428e-04	2.539233e-04	other	41
aneura	1.094589e-05	4.233587e-06	-6.712300e-06	other	826
anfractuosa	5.650814e-04	5.545739e-04	-1.050747e-05	other	16
angusta	5.650814e-04	1.156121e-03	5.910400e-04	other	16
anthochaera	4.758580e-04	1.535120e-04	-3.223460e-04	other	19
aphanoclada	2.260326e-03	6.950566e-03	4.690240e-03	paleo-endemic	4
aphylla	3.013767e-03	1.205790e-02	9.044135e-03	paleo-endemic	3
applanata	2.825407e-04	2.195575e-03	1.913034e-03	other	32
aprepta	2.825407e-04	2.234867e-08	-2.825183e-04	other	32
aprica	9.041302e-03	2.293302e-03	-6.748001e-03	neo-endemic	1
araneosa	3.013767e-03	9.974512e-04	-2.016316e-03	neo-endemic	3
arcuatilis	1.506884e-03	1.846046e-04	-1.322279e-03	neo-endemic	6
areolata	1.291615e-03	1.021653e-07	-1.291512e-03	neo-endemic	7
argutifolia	3.013767e-03	5.086852e-03	2.073085e-03	meso-endemic	3
argyraea	1.705906e-04	3.020693e-04	1.314787e-04	other	53
argyrodendron	3.767209e-04	5.441393e-05	-3.223070e-04	other	24
argyrophylla	2.825407e-04	2.648535e-05	-2.560553e-04	other	32
arida	2.583229e-04	7.907022e-05	-1.792527e-04	other	35
armitii	6.027535e-04	9.113025e-05	-5.116232e-04	other	15
arrecta	6.458073e-04	3.388787e-04	-3.069286e-04	other	14
ashbyae	9.041302e-04	3.632112e-03	2.727981e-03	other	10
aspera	3.013767e-04	8.807847e-05	-2.132983e-04	other	30
asperulacea	1.965500e-04	1.088829e-04	-8.766714e-05	other	46
assimilis	1.051314e-04	3.533684e-05	-6.979458e-05	other	86
ataxiphylla	1.004589e-03	3.729739e-03	2.725149e-03	meso-endemic	9
atkinsiana	4.758580e-04	6.310771e-04	1.552191e-04	other	19
atrox	9.041302e-03	5.763760e-03	-3.277542e-03	neo-endemic	1
attenuata	8.219366e-04	8.138164e-04	-8.120184e-06	other	11
aulacocarpa	1.051314e-04	5.081359e-05	-5.431783e-05	other	86
aulacophylla	5.022946e-04	2.950568e-04	-2.072377e-04	other	18
auratiflora	3.013767e-03	1.277168e-02	9.757913e-03	paleo-endemic	3
aureocrinita	1.004589e-03	3.679445e-04	-6.366446e-04	neo-endemic	9
auricoma	1.506884e-03	2.956324e-03	1.449440e-03	meso-endemic	6
auriculiformis	8.371576e-05	2.505897e-04	1.668740e-04	other	108
auronitens	7.534419e-04	1.084753e-03	3.313107e-04	other	12
ausfeldii	6.027535e-04	4.767715e-08	-6.027058e-04	other	15
axillaris	8.219366e-04	2.547651e-03	1.725715e-03	other	11
ayersiana	7.597733e-05	5.308986e-05	-2.288747e-05	other	119
baeuerlenii	6.027535e-04	6.645766e-04	6.182310e-05	other	15
baileyana	7.534419e-04	1.348035e-04	-6.186383e-04	other	12
bakeri	6.027535e-04	2.710215e-03	2.107461e-03	other	15
balsamea	4.758580e-04	1.138892e-04	-3.619688e-04	other	19
bancroftiorum	1.144469e-04	9.052624e-09	-1.144378e-04	other	79
barattensis	3.013767e-03	7.124868e-03	4.111101e-03	paleo-endemic	3
barringtonensis	1.291615e-03	1.517307e-03	2.256928e-04	meso-endemic	7
bartleana	4.520651e-03	3.575787e-07	-4.520294e-03	neo-endemic	2
basedowii	3.229037e-04	6.167092e-04	2.938056e-04	other	28
baueri	3.616521e-04	9.258915e-04	5.642394e-04	other	25
baxteri	1.004589e-03	3.822126e-03	2.817537e-03	meso-endemic	9
beadleana	4.520651e-03	1.375688e-02	9.236227e-03	paleo-endemic	2
beauverdiana	3.477424e-04	4.052933e-05	-3.072131e-04	other	26
beckleri	1.674315e-04	1.375453e-04	-2.988619e-05	other	54
benthamii	1.130163e-03	3.384190e-04	-7.917438e-04	neo-endemic	8
betchei	7.534419e-04	2.232045e-04	-5.302373e-04	other	12
bidentata	1.674315e-04	4.496156e-04	2.821841e-04	other	54
bifaria	1.506884e-03	7.277486e-04	-7.791351e-04	neo-endemic	6
biflora	6.954848e-04	1.195532e-03	5.000474e-04	other	13
binata	7.534419e-04	4.722852e-04	-2.811566e-04	other	12
binervata	2.318283e-04	2.074490e-05	-2.110834e-04	other	39
binervia	3.348630e-04	2.209943e-04	-1.138688e-04	other	27
bivenosa	4.589494e-05	7.553815e-05	2.964321e-05	other	197
blakei	1.027421e-04	6.605827e-04	5.578406e-04	other	88
blakelyi	3.013767e-04	4.671436e-04	1.657668e-04	other	30
blayana	3.013767e-03	5.415040e-04	-2.472263e-03	neo-endemic	3
boormanii	4.520651e-04	5.757662e-04	1.237011e-04	other	20
brachybotrya	7.063517e-05	1.471852e-05	-5.591666e-05	other	128
brachyclada	3.616521e-04	1.175988e-03	8.143363e-04	other	25
brachyphylla	5.022946e-04	8.905851e-04	3.882905e-04	other	18
brachypoda	1.808260e-03	2.244015e-03	4.357546e-04	meso-endemic	5
brachystachya	3.798867e-05	2.516907e-05	-1.281959e-05	other	238
bracteolata	1.291615e-03	1.436652e-03	1.450379e-04	meso-endemic	7
brassii	5.318413e-04	1.158921e-03	6.270801e-04	other	17
brockii	1.004589e-03	2.920476e-03	1.915887e-03	meso-endemic	9
bromilowiana	1.004589e-03	1.770965e-03	7.663762e-04	meso-endemic	9
browniana	2.318283e-04	4.626265e-04	2.307982e-04	other	39
bulgaensis	2.260326e-03	6.533363e-04	-1.606989e-03	neo-endemic	4
burkittii	4.346780e-05	2.406079e-05	-1.940701e-05	other	208
buxifolia	7.233042e-05	1.773820e-04	1.050516e-04	other	125
caesiella	4.520651e-04	1.115294e-03	6.632290e-04	other	20
calamifolia	9.132629e-05	5.216657e-05	-3.915971e-05	other	99
calantha	1.130163e-03	1.174237e-03	4.407440e-05	meso-endemic	8
calcicola	8.693560e-05	8.214313e-06	-7.872129e-05	other	104
camptoclada	2.825407e-04	4.585115e-04	1.759708e-04	other	32
campylophylla	1.291615e-03	2.723046e-03	1.431431e-03	meso-endemic	7
cana	3.616521e-04	1.285143e-04	-2.331378e-04	other	25
cangaiensis	4.520651e-03	4.788326e-04	-4.041819e-03	neo-endemic	2
cardiophylla	5.650814e-04	2.138152e-04	-3.512662e-04	other	16
carneorum	3.229037e-04	7.078652e-04	3.849615e-04	other	28
carnosula	2.260326e-03	5.380049e-03	3.119724e-03	paleo-endemic	4
caroleae	1.189645e-04	4.981365e-05	-6.915085e-05	other	76
catenulata	1.532424e-04	8.273224e-05	-7.051017e-05	other	59
cavealis	1.506884e-03	3.656891e-03	2.150007e-03	meso-endemic	6
cedroides	1.808260e-03	4.662524e-03	2.854264e-03	meso-endemic	5
celastrifolia	4.305382e-04	8.602593e-04	4.297211e-04	other	21
celsa	6.027535e-04	4.034102e-04	-1.993433e-04	other	15
chartacea	5.318413e-04	5.367480e-04	4.906701e-06	other	17
cheelii	2.659207e-04	1.809641e-04	-8.495653e-05	other	34
chinchillensis	1.004589e-03	1.711744e-03	7.071549e-04	meso-endemic	9
chisholmii	1.349448e-04	1.067399e-08	-1.349341e-04	other	67
chrysotricha	4.520651e-03	5.207260e-03	6.866087e-04	meso-endemic	2
citrinoviridis	1.772804e-04	2.045159e-05	-1.568289e-04	other	51
clelandii	2.205196e-04	1.818532e-04	-3.866640e-05	other	41
clunies	4.520651e-03	1.818097e-03	-2.702554e-03	neo-endemic	2
clydonophora	1.506884e-03	3.188094e-03	1.681211e-03	meso-endemic	6
cochlearis	1.705906e-04	2.671033e-04	9.651273e-05	other	53
cognata	5.650814e-04	1.802979e-04	-3.847834e-04	other	16
colei	4.684613e-05	3.705478e-09	-4.684242e-05	other	193
colletioides	5.833098e-05	1.279603e-04	6.962930e-05	other	155
complanata	1.130163e-04	2.001405e-04	8.712423e-05	other	80
concurrens	1.883605e-04	2.784710e-05	-1.605134e-04	other	48
conferta	8.951784e-05	6.448137e-05	-2.503648e-05	other	101
congesta	6.027535e-04	2.049068e-03	1.446315e-03	other	15
consobrina	6.954848e-04	1.281995e-03	5.865106e-04	other	13
conspersa	3.013767e-04	4.142095e-04	1.128328e-04	other	30
constablei	9.041302e-03	3.623151e-02	2.719021e-02	paleo-endemic	1
continua	1.329603e-04	1.970188e-04	6.405845e-05	other	68
coolgardiensis	1.015877e-04	1.628077e-04	6.122007e-05	other	89
coriacea	4.709012e-05	3.724778e-09	-4.708639e-05	other	192
costata	8.219366e-04	2.172506e-03	1.350569e-03	other	11
courtii	9.041302e-03	6.064724e-03	-2.976578e-03	neo-endemic	1
covenyi	1.506884e-03	3.143006e-03	1.636122e-03	meso-endemic	6
cowleana	6.150546e-05	1.050247e-04	4.351924e-05	other	147
craspedocarpa	2.009178e-04	7.912177e-05	-1.217961e-04	other	45
crassa	9.418023e-05	2.596380e-05	-6.821644e-05	other	96
crassicarpa	1.482181e-04	1.172389e-08	-1.482063e-04	other	61
crassiuscula	6.027535e-04	6.187676e-04	1.601409e-05	other	15
cremiflora	6.458073e-04	5.288980e-04	-1.169093e-04	other	14
cultriformis	2.318283e-04	3.304559e-04	9.862759e-05	other	39
cupularis	8.001152e-05	3.408263e-05	-4.592890e-05	other	113
curranii	6.954848e-04	1.242627e-03	5.471421e-04	other	13
curvata	1.130163e-03	2.382844e-03	1.252681e-03	meso-endemic	8
cuspidifolia	3.117690e-04	2.172394e-03	1.860625e-03	other	29
cuthbertsonii	5.446568e-05	1.962704e-04	1.418048e-04	other	166
cyclops	1.369894e-04	6.521050e-04	5.151156e-04	other	66
cyperophylla	9.827502e-05	7.358728e-05	-2.468775e-05	other	92
dangarensis	4.520651e-03	7.451232e-03	2.930580e-03	paleo-endemic	2
dawsonii	3.013767e-04	8.916427e-04	5.902659e-04	other	30
dealbata	5.758791e-05	1.093708e-05	-4.665083e-05	other	157
deanei	4.834921e-05	8.730399e-06	-3.961881e-05	other	187
debilis	6.027535e-04	5.539579e-04	-4.879562e-05	other	15
declinata	2.260326e-03	1.914810e-03	-3.455160e-04	meso-endemic	4
decora	3.645686e-05	5.971545e-05	2.325858e-05	other	248
decurrens	4.520651e-04	3.575787e-08	-4.520294e-04	other	20
delibrata	4.520651e-04	3.778769e-04	-7.418819e-05	other	20
delphina	7.534419e-04	4.314350e-03	3.560908e-03	other	12
dempsteri	5.022946e-04	1.809224e-03	1.306929e-03	other	18
denticulosa	1.130163e-03	2.010220e-04	-9.291408e-04	neo-endemic	8
desmondii	1.291615e-03	3.279173e-04	-9.636973e-04	neo-endemic	7
diallaga	4.520651e-03	3.270479e-04	-4.193603e-03	neo-endemic	2
dictyoneura	4.520651e-03	3.307158e-03	-1.213493e-03	neo-endemic	2
dictyophleba	4.783758e-05	6.060738e-05	1.276980e-05	other	189
didyma	4.520651e-03	3.575787e-07	-4.520294e-03	neo-endemic	2
difficilis	7.597733e-05	2.232075e-05	-5.365658e-05	other	119
dimidiata	1.159141e-04	2.612836e-04	1.453694e-04	other	78
diphylla	2.260326e-03	2.374963e-03	1.146377e-04	meso-endemic	4
disparrima	1.063683e-04	7.301173e-05	-3.335653e-05	other	85
distans	6.027535e-04	5.797399e-04	-2.301357e-05	other	15
dodonaeifolia	4.520651e-04	6.012640e-04	1.491989e-04	other	20
dolichophylla	2.260326e-03	1.087952e-02	8.619193e-03	paleo-endemic	4
doratoxylon	8.219366e-05	1.726829e-04	9.048922e-05	other	110
dorothea	9.041302e-04	9.912609e-04	8.713065e-05	other	10
drepanocarpa	9.618407e-05	2.137020e-05	-7.481386e-05	other	94
drepanophylla	1.130163e-03	3.155062e-03	2.024899e-03	meso-endemic	8
drummondii	2.443595e-04	2.884462e-04	4.408670e-05	other	37
dunnii	2.659207e-04	3.924237e-04	1.265030e-04	other	34
elachantha	5.870976e-05	4.643879e-09	-5.870511e-05	other	154
elata	6.027535e-04	1.707331e-03	1.104577e-03	other	15
elongata	2.443595e-04	1.557358e-04	-8.862377e-05	other	37
empelioclada	1.808260e-03	2.101121e-03	2.928602e-04	meso-endemic	5
enervia	1.965500e-04	1.332475e-04	-6.330260e-05	other	46
enterocarpa	5.650814e-04	3.796834e-04	-1.853980e-04	other	16
epacantha	3.013767e-03	5.409387e-03	2.395620e-03	meso-endemic	3
eriopoda	1.586193e-04	7.484815e-04	5.898622e-04	other	57
errabunda	1.506884e-03	1.690419e-03	1.835348e-04	meso-endemic	6
estrophiolata	8.529530e-05	1.650084e-04	7.971307e-05	other	106
euthycarpa	1.144469e-04	9.517772e-05	-1.926914e-05	other	79
excelsa	4.783758e-05	1.838210e-05	-2.945548e-05	other	189
excentrica	9.041302e-04	7.147236e-04	-1.894066e-04	other	10
exilis	1.506884e-03	2.421227e-03	9.143433e-04	meso-endemic	6
extensa	3.348630e-04	1.416159e-03	1.081296e-03	other	27
fagonioides	1.808260e-03	3.241301e-03	1.433041e-03	meso-endemic	5
falcata	1.015877e-04	8.035475e-09	-1.015796e-04	other	89
falciformis	7.662121e-05	4.818377e-05	-2.843744e-05	other	118
faucium	1.808260e-03	7.259642e-04	-1.082296e-03	neo-endemic	5
fecunda	2.260326e-03	2.142886e-03	-1.174394e-04	meso-endemic	4
filicifolia	2.205196e-04	8.371121e-05	-1.368084e-04	other	41
fimbriata	1.027421e-04	1.739064e-05	-8.535143e-05	other	88
flexifolia	1.923681e-04	1.761718e-04	-1.619629e-05	other	47
floribunda	1.076346e-04	1.947956e-04	8.716102e-05	other	84
fragilis	2.102628e-04	1.452862e-04	-6.497662e-05	other	43
fulva	1.808260e-03	1.324531e-03	-4.837296e-04	meso-endemic	5
gardneri	9.041302e-04	3.984442e-04	-5.056860e-04	other	10
gelasina	2.260326e-03	2.557917e-03	2.975918e-04	meso-endemic	4
genistifolia	7.794226e-05	1.178698e-04	3.992756e-05	other	116
georginae	8.951784e-05	1.434821e-04	5.396423e-05	other	101
gilbertii	6.954848e-04	5.578852e-04	-1.375996e-04	other	13
gillii	2.260326e-03	2.516661e-03	2.563354e-04	meso-endemic	4
gittinsii	9.041302e-04	1.741342e-03	8.372116e-04	other	10
gladiiformis	2.825407e-04	5.708986e-04	2.883579e-04	other	32
glaucissima	1.808260e-03	2.314075e-03	5.058149e-04	meso-endemic	5
glaucocarpa	2.054841e-04	5.059367e-05	-1.548905e-04	other	44
glaucoptera	3.013767e-04	1.167353e-04	-1.846415e-04	other	30
gnidium	5.318413e-04	3.441696e-04	-1.876717e-04	other	17
gonocarpa	1.532424e-04	8.435170e-04	6.902746e-04	other	59
gonoclada	6.747241e-05	7.179551e-05	4.323101e-06	other	134
gonophylla	3.931001e-04	1.091335e-03	6.982349e-04	other	23
gracillima	6.954848e-04	1.270560e-03	5.750757e-04	other	13
grandifolia	1.808260e-03	7.015666e-04	-1.106694e-03	neo-endemic	5
grasbyi	1.458275e-04	4.361439e-05	-1.022131e-04	other	62
gregorii	6.954848e-04	3.589778e-03	2.894294e-03	other	13
guinetii	4.520651e-03	9.092776e-04	-3.611374e-03	neo-endemic	2
hakeoides	4.367779e-05	4.211422e-05	-1.563566e-06	other	207
halliana	1.458275e-04	2.542503e-04	1.084229e-04	other	62
hamersleyensis	4.520651e-04	6.616731e-04	2.096080e-04	other	20
hammondii	6.504534e-05	2.785352e-05	-3.719182e-05	other	139
harpophylla	5.581051e-05	1.330010e-05	-4.251040e-05	other	162
harveyi	6.027535e-04	2.449039e-04	-3.578496e-04	other	15
hastulata	6.458073e-04	5.108267e-08	-6.457562e-04	other	14
havilandiorum	1.390970e-04	1.968137e-04	5.771677e-05	other	65
hemiteles	1.174195e-04	4.068415e-05	-7.673536e-05	other	77
hemsleyi	1.027421e-04	5.079073e-04	4.051652e-04	other	88
heterochroa	1.130163e-03	2.699382e-03	1.569219e-03	meso-endemic	8
heteroclita	3.931001e-04	1.466908e-04	-2.464093e-04	other	23
hexaneura	1.506884e-03	1.080625e-03	-4.262588e-04	meso-endemic	6
hilliana	5.226186e-05	2.153507e-05	-3.072679e-05	other	173
holosericea	2.613093e-05	2.066929e-09	-2.612886e-05	other	346
hopperiana	1.004589e-03	7.946192e-08	-1.004510e-03	neo-endemic	9
howittii	6.458073e-04	1.393318e-04	-5.064755e-04	other	14
huegelii	4.109683e-04	1.370513e-03	9.595443e-04	other	22
hylonoma	4.520651e-03	5.013222e-02	4.561157e-02	paleo-endemic	2
hypermeces	9.041302e-03	1.074410e-02	1.702800e-03	meso-endemic	1
hystrix	7.534419e-04	2.479453e-03	1.726011e-03	other	12
imbricata	1.808260e-03	1.385359e-03	-4.229019e-04	meso-endemic	5
implexa	4.636565e-05	1.265693e-04	8.020368e-05	other	195
inaequilatera	1.051314e-04	1.812082e-04	7.607677e-05	other	86
inceana	3.229037e-04	4.895622e-04	1.666585e-04	other	28
incrassata	1.004589e-03	5.700825e-03	4.696236e-03	paleo-endemic	9
ingramii	1.808260e-03	1.707909e-03	-1.003520e-04	meso-endemic	5
irrorata	1.130163e-04	1.884847e-05	-9.416781e-05	other	80
iteaphylla	2.659207e-04	5.605052e-04	2.945845e-04	other	34
ixiophylla	1.435127e-04	9.767902e-05	-4.583372e-05	other	63
ixodes	2.583229e-04	6.562958e-04	3.979728e-04	other	35
jacksonioides	7.534419e-04	4.555974e-03	3.802532e-03	other	12
jamesiana	3.767209e-04	6.491544e-04	2.724335e-04	other	24
jennerae	1.089314e-04	1.572901e-04	4.835874e-05	other	83
jensenii	4.758580e-04	1.711435e-03	1.235577e-03	other	19
jibberdingensis	3.616521e-04	8.985223e-05	-2.717999e-04	other	25
jonesii	1.506884e-03	2.427876e-04	-1.264096e-03	neo-endemic	6
jucunda	2.739789e-04	8.145215e-05	-1.925267e-04	other	33
julifera	6.412271e-05	8.789517e-05	2.377246e-05	other	141
karina	3.013767e-03	7.506891e-04	-2.263078e-03	neo-endemic	3
kempeana	3.531759e-05	9.497744e-06	-2.581984e-05	other	256
kybeanensis	7.534419e-04	5.959644e-08	-7.533823e-04	other	12
lamprocarpa	8.693560e-05	2.295724e-05	-6.397836e-05	other	104
lasiocalyx	1.159141e-04	2.462542e-04	1.303401e-04	other	78
latescens	1.738712e-04	3.170681e-04	1.431969e-04	other	52
latipes	1.643873e-04	1.300286e-08	-1.643743e-04	other	55
latisepala	1.130163e-03	3.808536e-03	2.678374e-03	meso-endemic	8
latzii	1.291615e-03	3.228034e-04	-9.688112e-04	neo-endemic	7
leiocalyx	5.413953e-05	2.768352e-05	-2.645602e-05	other	167
leioderma	9.041302e-04	1.165093e-03	2.609632e-04	other	10
leptocarpa	5.686354e-05	9.001330e-05	3.314976e-05	other	159
leptoneura	2.260326e-03	1.281589e-02	1.055557e-02	paleo-endemic	4
leucoclada	1.643873e-04	1.453395e-04	-1.904778e-05	other	55
leucolobia	4.109683e-04	4.408154e-04	2.984710e-05	other	22
ligulata	1.323763e-05	9.300050e-06	-3.937582e-06	other	683
linearifolia	4.109683e-04	2.357883e-04	-1.751800e-04	other	22
lineata	9.132629e-05	1.283840e-05	-7.848788e-05	other	99
lineolata	2.152691e-04	3.269648e-04	1.116957e-04	other	42
linifolia	6.458073e-04	3.512223e-04	-2.945850e-04	other	14
loderi	2.152691e-04	1.507607e-04	-6.450838e-05	other	42
longifolia	5.581051e-05	2.908240e-05	-2.672810e-05	other	162
longispicata	1.089314e-04	5.300418e-04	4.211105e-04	other	83
longispinea	1.674315e-04	6.547099e-04	4.872784e-04	other	54
longissima	2.659207e-04	2.123246e-04	-5.359605e-05	other	34
loroloba	7.534419e-04	5.959644e-08	-7.533823e-04	other	12
lycopodiifolia	1.051314e-04	1.541408e-04	4.900937e-05	other	86
lysiphloia	4.453843e-05	1.610651e-05	-2.843192e-05	other	203
mabellae	6.954848e-04	3.001854e-04	-3.952994e-04	other	13
macdonnellensis	1.643873e-04	4.268892e-04	2.625019e-04	other	55
mackeyana	3.348630e-04	1.928347e-04	-1.420283e-04	other	27
macnuttiana	3.013767e-03	1.542803e-03	-1.470965e-03	neo-endemic	3
maconochieana	7.534419e-04	1.152927e-03	3.994852e-04	other	12
macradenia	2.009178e-04	3.950635e-04	1.941457e-04	other	45
maitlandii	4.612909e-05	1.546431e-04	1.085140e-04	other	196
mangium	4.305382e-04	3.405511e-08	-4.305041e-04	other	21
maranoensis	1.808260e-03	2.106996e-04	-1.597561e-03	neo-endemic	5
marramamba	3.477424e-04	7.539219e-04	4.061795e-04	other	26
masliniana	4.109683e-04	5.798577e-04	1.688894e-04	other	22
mearnsii	8.610764e-05	8.187043e-06	-7.792060e-05	other	105
meisneri	8.219366e-04	7.637584e-05	-7.455607e-04	other	11
melanoxylon	3.675326e-05	3.684181e-05	8.855239e-08	other	246
melleodora	3.477424e-05	4.303471e-05	8.260466e-06	other	260
melvillei	7.727609e-05	6.112456e-09	-7.726998e-05	other	117
menzelii	1.291615e-03	2.194496e-03	9.028818e-04	meso-endemic	7
microbotrya	1.205507e-04	9.535431e-09	-1.205412e-04	other	75
microsperma	3.477424e-04	2.750605e-08	-3.477149e-04	other	26
midgleyi	5.650814e-04	3.779706e-04	-1.871108e-04	other	16
minyura	9.320930e-05	7.372756e-09	-9.320193e-05	other	97
mitchellii	3.117690e-04	5.187479e-04	2.069788e-04	other	29
mollifolia	5.022946e-04	2.514377e-04	-2.508569e-04	other	18
montana	9.132629e-05	9.294433e-05	1.618047e-06	other	99
monticola	4.147386e-05	1.021761e-04	6.070219e-05	other	218
mountfordiae	9.041302e-04	1.821124e-03	9.169935e-04	other	10
mucronata	1.015877e-04	5.603221e-05	-4.555546e-05	other	89
muelleriana	3.616521e-04	2.709322e-04	-9.071989e-05	other	25
multisiliqua	6.954848e-05	2.406957e-05	-4.547891e-05	other	130
multispicata	1.174195e-04	4.138899e-05	-7.603052e-05	other	77
murrayana	2.546846e-05	1.724498e-05	-8.223474e-06	other	355
myrtifolia	4.224908e-05	1.265250e-04	8.427592e-05	other	214
nanodealbata	6.458073e-04	2.465476e-04	-3.992597e-04	other	14
nematophylla	5.022946e-04	5.499166e-04	4.762204e-05	other	18
neriifolia	8.951784e-05	6.424201e-05	-2.527583e-05	other	101
neurocarpa	1.189645e-04	9.409965e-09	-1.189551e-04	other	76
neurophylla	1.329603e-04	2.234059e-04	9.044556e-05	other	68
nigricans	1.291615e-03	5.549989e-04	-7.366157e-04	neo-endemic	7
notabilis	1.390970e-04	6.622633e-05	-7.287063e-05	other	65
nuperrima	1.310334e-04	3.760527e-04	2.450193e-04	other	69
nyssophylla	5.833098e-05	3.367499e-05	-2.465600e-05	other	155
obliquinervia	1.586193e-04	1.016175e-04	-5.700188e-05	other	57
obtecta	8.219366e-04	4.835386e-03	4.013450e-03	other	11
obtusata	5.022946e-04	7.312644e-04	2.289698e-04	other	18
obtusifolia	2.205196e-04	3.417648e-04	1.212452e-04	other	41
oldfieldii	9.041302e-04	2.881355e-04	-6.159947e-04	other	10
olgana	4.305382e-04	3.790987e-04	-5.143951e-05	other	21
olsenii	4.520651e-03	3.575787e-07	-4.520294e-03	neo-endemic	2
omalophylla	8.001152e-05	6.328826e-09	-8.000520e-05	other	113
oncinocarpa	1.329603e-04	3.338679e-04	2.009076e-04	other	68
oncinophylla	8.219366e-04	7.309440e-05	-7.488422e-04	other	11
orites	2.260326e-03	2.625937e-03	3.656117e-04	meso-endemic	4
orthocarpa	8.951784e-05	1.358819e-04	4.636407e-05	other	101
oshanesii	3.616521e-04	1.789506e-04	-1.827015e-04	other	25
oswaldii	1.458275e-05	9.687391e-06	-4.895354e-06	other	620
oxyclada	1.506884e-03	8.782038e-04	-6.286799e-04	neo-endemic	6
pachyacra	1.390970e-04	5.051913e-05	-8.857783e-05	other	65
pachycarpa	4.758580e-04	4.834945e-04	7.636480e-06	other	19
papyrocarpa	8.072591e-05	1.342818e-04	5.355590e-05	other	112
paradoxa	4.783758e-05	7.922931e-05	3.139173e-05	other	189
paraneura	7.350652e-05	5.814287e-09	-7.350071e-05	other	123
parramattensis	2.916549e-04	4.829998e-05	-2.433549e-04	other	31
parvipinnula	5.650814e-04	4.284248e-04	-1.366566e-04	other	16
patagiata	4.305382e-04	5.534962e-04	1.229580e-04	other	21
pedina	3.013767e-03	2.383858e-07	-3.013529e-03	neo-endemic	3
pedleyi	2.260326e-03	7.846074e-04	-1.475718e-03	neo-endemic	4
pellita	2.009178e-04	7.261772e-05	-1.283001e-04	other	45
pendula	6.278682e-05	6.050484e-05	-2.281981e-06	other	144
penninervis	5.758791e-05	8.286367e-06	-4.930155e-05	other	157
pentadenia	7.534419e-04	1.364025e-03	6.105834e-04	other	12
perangusta	1.808260e-03	2.316820e-04	-1.576578e-03	neo-endemic	5
perryi	3.348630e-04	2.962376e-04	-3.862548e-05	other	27
petraea	2.511473e-04	1.252748e-04	-1.258725e-04	other	36
peuce	5.022946e-04	2.197851e-03	1.695556e-03	other	18
phlebopetala	1.130163e-03	2.418246e-03	1.288083e-03	meso-endemic	8
phlebophylla	3.013767e-03	4.054453e-03	1.040686e-03	meso-endemic	3
pickardii	1.291615e-03	2.351349e-03	1.059734e-03	meso-endemic	7
platycarpa	4.834921e-05	2.732997e-05	-2.101924e-05	other	187
plectocarpa	6.235381e-05	8.240310e-06	-5.411350e-05	other	145
podalyriifolia	1.923681e-04	9.966441e-05	-9.270372e-05	other	47
polybotrya	2.511473e-04	5.839846e-05	-1.927488e-04	other	36
porcata	4.520651e-03	1.314903e-02	8.628374e-03	paleo-endemic	2
praelongata	6.458073e-04	1.262040e-03	6.162324e-04	other	14
prainii	1.015877e-04	3.567385e-05	-6.591382e-05	other	89
pravifolia	1.329603e-04	1.334828e-04	5.224732e-07	other	68
pravissima	2.443595e-04	2.324177e-04	-1.194185e-05	other	37
producta	3.229037e-04	5.648936e-04	2.419900e-04	other	28
proiantha	1.506884e-03	1.090421e-03	-4.164631e-04	meso-endemic	6
prominens	9.041302e-04	7.151573e-08	-9.040587e-04	other	10
pruinocarpa	7.794226e-05	3.362288e-04	2.582865e-04	other	116
pruinosa	3.931001e-04	3.719814e-05	-3.559020e-04	other	23
pterocaulon	4.520651e-03	9.952641e-03	5.431989e-03	paleo-endemic	2
ptychophylla	3.616521e-04	2.377486e-04	-1.239035e-04	other	25
pubicosta	1.808260e-03	7.195957e-03	5.387696e-03	paleo-endemic	5
pubifolia	1.506884e-03	1.191929e-07	-1.506765e-03	neo-endemic	6
pulchella	1.089314e-04	7.289968e-04	6.200654e-04	other	83
pustula	2.825407e-04	4.289275e-05	-2.396479e-04	other	32
pycnantha	5.795707e-05	1.078601e-05	-4.717106e-05	other	156
pycnostachya	1.506884e-03	2.223805e-03	7.169213e-04	meso-endemic	6
pygmaea	4.520651e-03	2.380212e-02	1.928147e-02	paleo-endemic	2
pyrifolia	8.001152e-05	6.323233e-05	-1.677920e-05	other	113
ramulosa	1.991476e-05	1.290480e-05	-7.009963e-06	other	454
redolens	6.458073e-04	1.287947e-03	6.421395e-04	other	14
repanda	4.520651e-03	7.566212e-04	-3.764030e-03	neo-endemic	2
retinervis	6.027535e-04	4.767715e-08	-6.027058e-04	other	15
retinodes	2.054841e-04	2.181258e-04	1.264167e-05	other	44
retivenea	6.849471e-05	4.696718e-05	-2.152754e-05	other	132
rhamphophylla	3.013767e-03	4.176572e-03	1.162805e-03	meso-endemic	3
rhetinocarpa	6.954848e-04	2.244641e-04	-4.710207e-04	other	13
rhigiophylla	5.650814e-04	1.189834e-04	-4.460980e-04	other	16
rhodophloia	8.449815e-05	3.112146e-05	-5.337669e-05	other	107
rigens	4.305382e-05	5.642081e-05	1.336699e-05	other	210
rivalis	5.022946e-04	3.403488e-04	-1.619458e-04	other	18
rostellifera	1.808260e-04	4.492540e-05	-1.359006e-04	other	50
rubida	9.827502e-05	1.802669e-04	8.199186e-05	other	92
ryaniana	1.808260e-03	1.853731e-03	4.547095e-05	meso-endemic	5
sabulosa	4.305382e-04	3.405511e-08	-4.305041e-04	other	21
saliciformis	1.004589e-03	1.850418e-04	-8.195474e-04	neo-endemic	9
salicina	2.366833e-05	2.181107e-05	-1.857257e-06	other	382
saligna	7.662121e-05	1.360768e-04	5.945559e-05	other	118
saxatilis	1.130163e-03	2.626446e-03	1.496284e-03	meso-endemic	8
schinoides	1.130163e-03	9.455330e-05	-1.035609e-03	neo-endemic	8
scirpifolia	4.758580e-04	3.763986e-08	-4.758204e-04	other	19
sclerophylla	8.219366e-05	1.332951e-04	5.110144e-05	other	110
semicircinalis	2.260326e-03	8.585448e-03	6.325123e-03	paleo-endemic	4
semilunata	5.022946e-04	1.195621e-03	6.933261e-04	other	18
semitrullata	1.130163e-03	3.372326e-03	2.242163e-03	meso-endemic	8
sericoflora	5.650814e-04	6.895197e-04	1.244383e-04	other	16
sericophylla	4.636565e-05	3.891030e-05	-7.455349e-06	other	195
sessilispica	3.348630e-04	3.605585e-04	2.569547e-05	other	27
shuttleworthii	6.954848e-04	1.689644e-03	9.941589e-04	other	13
sibilans	4.305382e-04	7.772306e-05	-3.528151e-04	other	21
sibina	2.379290e-04	2.373277e-04	-6.012703e-07	other	38
siculiformis	1.586193e-04	3.059698e-03	2.901079e-03	other	57
silvestris	5.650814e-04	1.294422e-04	-4.356392e-04	other	16
simsii	7.862002e-05	4.165739e-05	-3.696263e-05	other	115
simulans	2.260326e-03	7.527814e-03	5.267489e-03	paleo-endemic	4
sparsiflora	1.390970e-04	3.417164e-04	2.026194e-04	other	65
spathulifolia	3.767209e-04	1.379207e-03	1.002486e-03	other	24
spectabilis	1.076346e-04	3.073131e-04	1.996785e-04	other	84
spinescens	1.089314e-04	7.008022e-04	5.918708e-04	other	83
spirorbis	5.318413e-04	2.272885e-04	-3.045528e-04	other	17
spongolitica	1.291615e-03	4.427067e-04	-8.489079e-04	neo-endemic	7
stanleyi	3.013767e-03	2.383858e-07	-3.013529e-03	neo-endemic	3
stenophylla	3.206136e-05	2.239887e-04	1.919274e-04	other	282
stigmatophylla	2.205196e-04	5.408354e-04	3.203159e-04	other	41
stipuligera	6.599491e-05	8.888522e-05	2.289031e-05	other	137
storyi	1.808260e-03	1.532003e-03	-2.762579e-04	meso-endemic	5
stowardii	6.797972e-05	8.512254e-06	-5.946746e-05	other	133
striatifolia	6.954848e-04	2.124921e-04	-4.829927e-04	other	13
strongylophylla	2.102628e-04	3.991736e-04	1.889108e-04	other	43
suaveolens	7.410904e-05	1.461522e-04	7.204318e-05	other	122
subrigida	1.291615e-03	3.111243e-03	1.819628e-03	meso-endemic	7
subsessilis	1.004589e-03	5.491490e-04	-4.554402e-04	meso-endemic	9
subtessarogona	6.027535e-04	2.441324e-04	-3.586211e-04	other	15
subulata	3.616521e-04	2.444876e-04	-1.171645e-04	other	25
sulcaticaulis	9.041302e-03	6.926541e-03	-2.114761e-03	neo-endemic	1
synchronicia	1.004589e-04	2.667700e-04	1.663111e-04	other	90
tarculensis	3.931001e-04	3.109380e-08	-3.930690e-04	other	23
telmica	3.013767e-03	4.678942e-03	1.665175e-03	meso-endemic	3
tenuinervis	1.130163e-03	4.150358e-04	-7.151270e-04	neo-endemic	8
tenuispica	8.219366e-04	1.834678e-03	1.012742e-03	other	11
tenuissima	4.166499e-05	7.344688e-05	3.178189e-05	other	217
tephrina	1.310334e-04	8.915926e-05	-4.187410e-05	other	69
terminalis	1.015877e-04	4.265061e-04	3.249185e-04	other	89
tessellata	1.291615e-03	6.442597e-04	-6.473549e-04	neo-endemic	7
tetragonophylla	1.931902e-05	3.665425e-05	1.733523e-05	other	468
thomsonii	2.739789e-04	2.331303e-05	-2.506658e-04	other	33
tindaleae	6.458073e-04	2.755623e-04	-3.702450e-04	other	14
torulosa	4.520651e-05	3.964312e-05	-5.563396e-06	other	200
trachycarpa	2.260326e-04	1.787893e-08	-2.260147e-04	other	40
trachyphloia	1.291615e-03	5.363242e-04	-7.552905e-04	neo-endemic	7
translucens	1.238535e-04	7.344421e-05	-5.040925e-05	other	73
triptera	1.329603e-04	1.211247e-04	-1.183564e-05	other	68
triquetra	3.477424e-04	1.480797e-04	-1.996627e-04	other	26
tropica	1.965500e-04	4.125094e-04	2.159593e-04	other	46
tumida	4.432011e-05	6.356455e-05	1.924444e-05	other	204
tysonii	3.477424e-04	2.221210e-04	-1.256214e-04	other	26
umbellata	8.072591e-05	1.546654e-04	7.393949e-05	other	112
umbraculiformis	3.477424e-04	2.750605e-08	-3.477149e-04	other	26
uncinata	9.041302e-04	1.062119e-03	1.579891e-04	other	10
undoolyana	2.260326e-03	3.808520e-04	-1.879474e-03	neo-endemic	4
validinervia	1.586193e-04	5.343143e-04	3.756950e-04	other	57
venulosa	3.013767e-04	1.216959e-04	-1.796809e-04	other	30
verniciflua	6.367114e-05	1.507505e-05	-4.859609e-05	other	142
verricula	2.009178e-04	2.818688e-04	8.095097e-05	other	45
vestita	5.022946e-04	3.078380e-04	-1.944566e-04	other	18
victoriae	1.511923e-05	4.273991e-05	2.762068e-05	other	598
viscidula	2.102628e-04	1.248679e-03	1.038416e-03	other	43
wanyu	3.013767e-04	6.499338e-05	-2.363834e-04	other	30
wattsiana	8.219366e-04	6.501430e-08	-8.218716e-04	other	11
wilhelmiana	1.076346e-04	7.749416e-05	-3.014039e-05	other	84
woodmaniorum	3.013767e-03	5.004156e-03	1.990388e-03	meso-endemic	3
xanthina	1.004589e-03	4.607729e-04	-5.438163e-04	neo-endemic	9
xiphophylla	1.923681e-04	2.993527e-04	1.069845e-04	other	47
yirrkallensis	3.117690e-04	5.226377e-04	2.108686e-04	other	29